Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage
Open accessAbstract
This article evaluates the present global condition of solar and wind energy adoption and explores their benefits and limitations in meeting energy needs. It examines the historical and evolutionary growth of solar and wind energy, global trends in the usage of renewable energy, and upcoming technologies, including floating solar and vertical‐axis wind turbines. The importance of smart grid technology and energy storage alternatives for enhancing the effectiveness and dependability of renewable energy is explored. In addition, the role of Electric Vehicles (EVs) in a modern smart grid has been assessed. Furthermore, the economic benefits, and most recent technological developments of solar and wind energy and their environmental and social ramifications. The potential of solar and wind energy to meet the increasing global energy demand and the problems and opportunities facing the renewable energy industry have shown excellent promise. Machine learning applications for solar and wind energy generation are vital for sustainable energy production. Machine learning can help in design, optimization, cost reduction, and, most importantly, in improving the efficacy of solar and wind energy, including advancing energy storage. This assessment is a crucial resource for policymakers, industry leaders, and researchers who aim to make the world cleaner and more sustainable. Ultimately, this review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.
AI snapshot
Written from the paper’s full text.
TL;DR
This review examines solar and wind energy adoption, smart grids, energy storage, EVs, and the role of machine learning in optimizing renewable energy systems. It argues that ML can improve forecasting, design, cost reduction, and storage integration, while intermittency, policy, and infrastructure barriers remain.
Key findings
- Solar and wind accounted for more than 50% of the global increase in electricity generation capacity in 2017, with solar installations surpassing combined new fossil fuel and nuclear capacities; renewable electricity consumption is projected to rise by two-thirds between 2020 and 2030, reaching nearly 80% of global electricity demand.
- Floating solar PV plants ranging from 52 kW to 2 MW can reduce levelized cost of energy by up to 85% with further research and technological advancement.
- ML case studies report strong performance: a CNN-LSTM-transformer solar forecasting model achieved RMSE 0.344 and MAE 0.393; random forest wind power forecasting reached R² 0.995 and MAE 7.048; a WT-ACO-FFANN model achieved daily mean MAPE 4.67%, NMAE 0.82%, and forecast skill 56.22%.
- Energy storage and EV integration findings include BESS placement optimization reducing energy storage capacity by 8.85% before capacity distribution and about 27% after; a hybrid storage system reached lowest cost of about 9.85 ȼ/kWh at 42% depth of discharge; and smart metering/charging can increase self-consumption by 45%.
Methods
- Narrative review evaluating global solar and wind energy adoption, emerging technologies, smart grids, energy storage, EV/V2G integration, and AI/ML applications.
- Evidence base consists of cited literature and two summary tables: Table 1 for ML applications to solar energy and Table 2 for ML applications to wind energy, listing techniques, datasets, performance metrics, and main results.
- Main analysis is qualitative synthesis of reported case studies, technical developments, economic figures, and environmental/social impacts; no primary data collection or systematic review protocol is described.
Limitations
- The paper states that solar and wind face intermittency, weather-dependent production, high initial investment costs, integration challenges, and policy/economic barriers; it also notes that AI research still needs further work on multi-objective power system optimization and that some technologies such as TENGs have modest output.
- The paper does not state explicit limitations of its own review methodology.
- Evident limitations include the narrative rather than systematic design, reliance on secondary cited studies, fragmented or missing substantive text in some sections (e.g., Sections 4.1 and 4.2), and tables that are partially truncated in the provided text.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Claims the paper makes
0 of 136 cited sources checked · 0 supported · 0 flagged
- Premisecitation
Machine learning algorithms have optimized wind farm planning, resulting in more efficient design and implementation.
“Moreover, machine learning algorithms have optimized wind farm planning, resulting in more efficient design and implementation (Wu et al., 2014).”
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Solar photovoltaics are the most cost-effective renewable energy source.
“Because solar photovoltaics are the most costeffective renewable energy source, their global deployed capacity has increased rapidly over the past few years (Ahmadi et al., 2018).”
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- Premisecitation
Recent innovations, such as a pinwheel that generates electricity continuously by combining triboelectrification and electromagnetic induction, demonstrate the potential to capture small quantities of wind energy.
“Recent innovations, such as a pinwheel that generates electricity continuously by combining triboelectrification and electromagnetic induction, demonstrate the potential to capture small quantities of wind energy (Guo et al., 2019).”
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Machine-learning techniques have been used to detect and localize solar panel faults, drastically reducing the time required to identify and rectify faulty cells.
“For instance, machine-learning techniques have been used to detect and localize solar panel faults, drastically reducing the time required to identify and rectify faulty cells (Ahan et al., 2021).”
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Elshafei et al. (2021) estimate that wind power, photovoltaic, and concentrated solar power will cost €50/MWh, €48/MWh, and €56/MWh in 2050, assuming a generation capacity of 2000 TWh/y.
“Elshafei et al. (2021) estimate that wind power, Photovoltaic, and Concentrated Solar Power will cost €50/MWh, €48/MWh, and €56/MWh in 2050, assuming a generation capacity of 2000 TWh/y.”
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- Interpretationreasoning
By leveraging the predictive and optimization capabilities of machine learning, solar and wind energy sources can be harnessed and managed more effectively, substantially contributing to the achievement of global energy demands and sustainable development objectives.
“By leveraging the predictive and optimization capabilities of machine learning, these renewable energy sources can be harnessed and managed more effectively, substantially contributing to the achievement of global energy demands and sustainable development objectives.”
Quote in the paperSubstantial claim - Interpretationreasoning
Machine learning has been increasingly applied to solar and wind energy systems to optimize their efficacy, lower costs, and improve their predictive capabilities.
“As a subset of AI, machine learning has been increasingly applied to solar and wind energy systems to optimize their efficacy, lower costs, and improve their predictive capabilities.”
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Between 2010 and 2015, solar PV prices decreased from $0.285 per kilowatt-hour to $0.126 per kilowatt-hour.
“Between 2010 and 2015, solar PV prices decreased from $0.285 per Kilowatt-hour to $0.126 per Kilowatt-hour (Ferreira et al., 2018;Hoppmann et al., 2014).”
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Research in wind turbine technology, such as blade design, vertical-axis wind turbines, and accurate wind speed forecasting, has the potential to improve wind farm performance and environmental compatibility.
“Research in wind turbine technology, such as blade design, vertical-axis wind turbines, and accurate wind speed forecasting, has the potential to improve wind farm performance and environmental compatibility (Hui et al., 2018;Sitharthan et al., 2018;Staid & Guikema, 2015;Vargas et al., 2019).”
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- Interpretationnone given
TENGs represent a viable energy conversion technology for wind energy collection.
“TENGs represent a viable energy conversion technology for wind energy collection.”
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Solar energy remains a more affordable option despite differences in industrial and residential electricity costs.
“Despite differences in industrial and residential electricity costs, solar energy remains a more affordable option (Couture & Gagnon, 2010).”
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Machine learning-based solar energy forecasting systems have demonstrated high accuracy.
“In addition, machine learning-based solar energy forecasting systems have demonstrated high accuracy (Partheeban et al., 2022).”
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- Interpretationreasoning
Machine learning techniques can improve the application and administration of renewable energy sources, making them more viable options for meeting the world's energy needs.
“Machine learning techniques can improve the application and administration of renewable energy sources, making them more viable options for meeting the world's energy needs.”
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Machine learning has shown promise in wind energy systems, where it can enhance wind energy forecasting and wind speed predictions.
“Similarly, machine learning has shown promise in wind energy systems, where it can enhance wind energy forecasting and wind speed predictions (Zhao, Sun, & Wang, 2022).”
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The SVR model showed excellent capability in wind speed forecasting with an RSE of 0.94.
“SVR model showed excellent capability in wind speed forecasting with an RSE of 0.94.”
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For the RBF method, the best prediction performance (R2) of 0.99827 and MSE of 0.000353 at 70 epochs was achieved using a Gaussian function.
“For the RBF method, the best prediction performance (R2) of 0.99827 and MSE of 0.000353 at 70 epochs was achieved using a Gaussian function.”
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Several Mediterranean basin zones with attractive offshore wind potential were identified, such as the Aegean Sea, Gulf of Lyon, Northern Morocco, and Tunisia regions.
“Identified several mediterranean basin zones with attractive offshore wind potential, such as the Aegean Sea, Gulf of Lyon, Northern Morocco, and Tunisia regions.”
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The hybrid model integrating machine learning and statistical approaches outperformed standard individual models and a model using only machine learning for predicting future solar energy generation in large-scale renewable energy plants.
“The proposed hybrid model outperformed standard individual models and a model using only machine learning.”
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For three-class classification, the best average success rates were 99.96% (RBFK) and 98.73% (SVM) at 900 rpm.
“For three-class classification, the best average success rates were 99.96% (RBFK) and 98.73% (SVM) at 900 rpm”
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The SVM classifier was able to identify 6 types of roof shapes with an average accuracy of 66%.
“The SVM classifier was able to identify 6 types of roof shapes with an average accuracy of 66%.”
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The combined forecasting model (WTD-LSTM) demonstrated a higher level of accuracy in predicting short-term wind speed compared to single models (MLP and LSTM).
“The combined forecasting model (WTD-LSTM) demonstrated a higher level of accuracy in predicting short-term wind speed compared to single models (MLP and LSTM).”
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For the MLP method, the best prediction performance (R2) of 0.99949 and MSE of 1.034e-4 at 77 epochs was achieved using the Bayesian Regularization backpropagation method.
“For the MLP method, the best prediction performance (R2) of 0.99949 and MSE of 1.034e-4 at 77 epochs was achieved using the Bayesian Regularization backpropagation method”
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The CNN-LSTM-transformer model had the lowest RMSE and MAE values of 0.344 and 0.393, respectively.
“The model had the lowest RMSE and MAE values of 0.344 and 0.393, respectively.”
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The solar tracking system, developed with machine learning, maximizes energy conversion efficiency by following the Sun's path.
“The solar tracking system, developed with machine learning, maximizes energy conversion efficiency by following the Sun's path.”
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The SVM classification method allowed for efficient targeting of common operating conditions and isolation of marginal phenomena in PV system performance analysis.
“The SVM classification method allowed for efficient targeting of common operating conditions and isolation of marginal phenomena in PV system performance analysis”
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ANN and GBRT were found to have the best performance compared to KNN and lasso regression for forecasting solar energy production in four cities.
“ANN and GBRT were found to have the best performance compared to other models (KNN and lasso regression).”
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The CNN-LSTM-transformer model achieved the highest accuracy compared to existing models and other combinations for solar energy production forecasting.
“The proposed CNN-LSTM-transformer model achieved the highest accuracy compared to existing models and other combinations.”
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The proposed method extends the predicted WTB blade coating fatigue life by 2.55 times.
“Proposed method extends the predicted WTB blade coating fatigue life by 2.55 times.”
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The temperature coefficient obtained from the SVM classification-based analysis was found to be reliable and comparable to values obtained by measurements according to IEC 61215.
“The temperature coefficient obtained from the SVM classification-based analysis was found to be reliable and comparable to values obtained by measurements according to IEC 61215”
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The use of nanomaterial (graphene) in solar panels increases solar harvesting capacity by 40% compared to silicon solar cells.
“The use of nanomaterial (graphene) in solar panels increases solar harvesting capacity by 40% compared to silicon solar cells.”
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There is a minor reduction in energy yield (0.027%) compared to the original rotor speed schedule.
“Minor reduction in energy yield (0.027%) compared to the original rotor speed schedule.”
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For seven-class classification, the best success rate was 98.83% (RBFK) at 900 rpm.
“For seven-class classification, the best success rate was 98.83% (RBFK) at 900 rpm”
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Including additional meteorological variables, especially sky cover, improves the accuracy of solar power prediction.
“Including additional meteorological variables, especially sky cover, improves the accuracy of solar power prediction.”
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Complex roofs had the highest average PV potential, despite having the second-lowest received mean solar radiation, due to having the largest cumulative roof area and available or useful roof area for PVs.
“Complex roofs had the highest average PV potential, despite having the second-lowest received mean solar radiation, due to having the largest cumulative roof area and available or useful roof area for PVs.”
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The neural network model was successful in predicting tether force for airborne wind energy systems without hyper-parameter tuning.
“Neural network model was successful in predicting tether force for AWE systems without hyper-parameter tuning.”
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The DNN model outperforms the persistent algorithm across all cases, achieving more accurate and reliable predictions of incoming solar irradiance.
“The DNN model outperforms the persistent algorithm across all cases, achieving more accurate and reliable predictions of incoming solar irradiance.”
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The random forest algorithm showed the best performance with an R2 value of 0.995 and MAE of 7.048 for wind power forecasting based on daily wind speed data.
“RF algorithm showed the best performance with an R 2 value of 0.995 and MAE of 7.048.”
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- Interpretationnone given
Solar and wind energy solutions can meet the rising energy demand.
“In conclusion, solar and wind energy solutions can meet the rising energy demand.”
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Wind energy can reduce fossil fuel pollution.
“Wind energy can reduce fossil fuel pollution (Pata et al., 2022;Sahu, 2018), whereas solar energy can be utilized for desalination, heating, and photocatalysis.”
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- Interpretationreasoning
Machine learning has shown great potential in driving the widespread adoption and increasing the feasibility of solar and wind energy for a sustainable future.
“Machine learning has shown great potential in driving the widespread adoption and increasing the feasibility of solar and wind energy for a sustainable future.”
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Coordinated charging, which optimizes charging times during low-demand periods, is an effective solution to mitigate these issues.
“Conversely, coordinated charging, which optimizes charging times during low-demand periods, is presented as an effective solution to mitigate these issues.”
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The economic viability of V2G is contingent upon charging/discharging strategies and vehicle aggregation.
“However, the economic viability of V2G is contingent upon charging/discharging strategies and vehicle aggregation.”
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Wind turbines' largely aerodynamic and mechanical noise can be intrusive, particularly if installed near residential areas.
“Wind turbines' largely aerodynamic and mechanical noise can be intrusive, particularly if installed near residential areas (Dhar et al., 2020).”
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Alternative storage technologies, such as pumped hydro and compressed air energy storage, offer promising opportunities due to their low environmental impact despite their energy density and geographical limitations.
“Alternative storage technologies, such as pumped hydro and compressed air energy storage, offer promising opportunities due to their low environmental impact despite their energy density and geographical limitations (Borri et al., 2020).”
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- Interpretationreasoning
AI can facilitate the integration of intermittent renewable energy sources with flexible dispatchable power, transmission connectivity, storage, smart infrastructure, and demand-side management.
“Integrating intermittent renewable energy sources with flexible dispatchable power, transmission connectivity, storage, smart infrastructure, and demand-side management can be facilitated by AI.”
Quote in the paperSubstantial claim- Rests on an assumption: The intermittent nature of solar and wind power is one of the primary obstacles to reaching the growing energy demand.
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Coordinated charging benefits EV owners and grid operators, especially with higher EV penetration.
“The paper underscores that coordinated charging benefits EV owners and grid operators, especially with higher EV penetration.”
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A single EV can add about 560-910 W to system load at peak times, influenced by the charger's size and the charging strategy.
“pointing out that a single EV can add about 560-910 W to system load at peak times, influenced by the charger's size and the charging strategy.”
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Smart metering and intelligent grid communication are essential for effective V2G operation.
“Smart metering and intelligent grid communication are deemed essential for effective V2G operation.”
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EVs contribute significantly to environmental sustainability by reducing emissions when replacing fossil-fuel vehicles.
“EVs contribute significantly to environmental sustainability by reducing emissions when replacing fossil-fuel vehicles.”
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The study estimates potential savings of $237 billion to $813 billion for the energy industry with smart ML automation.
“It estimates potential savings of $237 billion to $813 billion for the energy industry with smart ML automation.”
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The research revealed the project's potential to prevent 35,525 tonnes of greenhouse gas emissions over a quarter-century.
“In addition, the research revealed the project's potential to prevent 35,525 tonnes of Greenhouse Gases (GHG) emissions over a quarter-century (Himri et al., 2020)”
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- Interpretationreasoning
AI can play a crucial role in addressing obstacles to renewable energy integration.
“In addressing these obstacles, artificial intelligence can play a crucial role.”
Quote in the paperSubstantial claim- Rests on an assumption: The intermittent nature of solar and wind power is one of the primary obstacles to reaching the growing energy demand.
- Premisecitation
Uncoordinated charging significantly impacts DNs, often leading to increased load on distribution elements like transformers, reducing grid reliability.
“Uncoordinated charging, lacking intelligent scheduling, significantly impacts DNs, often leading to increased load on distribution elements like transformers, thereby reducing grid reliability.”
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- Interpretationcitation
AI-driven optimization techniques can increase the efficacy and cost-effectiveness of renewable energy systems, allowing them to compete with fossil fuel-based energy sources more effectively.
“AI-driven optimization techniques can increase the efficacy and cost-effectiveness of renewable energy systems, allowing them to compete with fossil fuel-based energy sources more effectively (Song et al., 2022).”
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Said and Alanazi's S2S LSTM neural network outperformed the baseline in energy forecasting.
“In energy forecasting, their S2S LSTM neural network outperformed the baseline, indicating the potential of deep learning models in this domain.”
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Publicly accessible charging points grew by 72% since 2015.
“With a 72% growth in publicly accessible charging points since 2015, government interventions are crucial for supporting this expansion.”
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V2G technologies have the potential to facilitate power exchange between EVs and the utility grid.
“This includes the potential of V2G technologies to facilitate power exchange between EVs and the utility grid.”
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Concerns arise regarding the grid's capacity to handle the charging and discharging of EV batteries, especially considering the need for improved charging technologies and smart metering.
“Concerns also arise regarding the grid's capacity to handle the charging and discharging of EV batteries, especially considering the need for improved charging technologies and smart metering.”
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HESS demonstrated optimum performance at 42% depth of discharge (DOD) for BESS, yielding the lowest cost of about 9.85 ȼ/kWh.
“HESS demonstrated optimum performance at 42% depth of discharge (DOD) for BESS, yielding the lowest cost of about 9.85 ȼ/kWh”
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Global EV sales surpassed 750,000 units in 2016.
“The rapid increase in EVs, as evidenced by global sales surpassing 750,000 units in 2016, necessitates a closer examination of their impact on power systems.”
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- Premiseown data
Solution 2 optimizes BESS capacity distribution, concentrating it in regions with lower line capacity, achieving an average of 82% concentration.
“The most effective solution, Solution 2, significantly optimizes BESS capacity distribution, primarily concentrating it in regions with lower line capacity, achieving an average of 82% concentration.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mohamad et al. (2021)
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AI-based prediction systems such as ANN, FLC, and adaptive neural fuzzy inference systems efficiently tackle control and prediction issues.
“They demonstrated that AI-based prediction systems, such as ANN, FLC, and adaptive neural fuzzy inference systems, efficiently tackle control and prediction issues.”
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Successful integration of EVs into smart grids depends on collaborative efforts of governments, power utilities, and EV owners.
“Overall, the successful integration of EVs into smart grids depends on the collaborative efforts of governments, power utilities, and EV owners.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ahmadi et al. (2019)
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Integrating micro-CAES into photovoltaic and wind energy systems enables effective demand shifting.
“Integrating micro-compressed air energy storage (micro-CAES) into photovoltaic and wind energy systems enables effective demand shifting (Arnaoutakis et al., 2023).”
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A bidirectional converter is effective in controlling power flow in both directions, from source to battery and battery to VSI, thereby compensating for grid voltage disturbances.
“The effectiveness of a bidirectional converter in controlling power flow in both directions, from the source to the battery and the battery to the voltage source inverter (VSI), thereby compensating for grid voltage disturbances, is a key finding.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Pattanaik et al. (2021)
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SESS exhibited minimum cost at 100% DOD, approximately 17.2 ȼ/kWh.
“SESS exhibited minimum cost at 100% DOD, approximately 17.2 ȼ/kWh.”
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- Premisecitation
Several factors, including operating temperature, depth of discharge, and charging/discharging currents, influence the lifespan of batteries in BESSs.
“Several factors, including operating temperature, depth of discharge, and charging/discharging currents, influence the lifespan of batteries in BESSs (Khalid Mehmood et al., 2017).”
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Advancements in battery technologies, including higher power density and larger capacities, are essential for sustainable deployment of EVs.
“Additionally, advancements in battery technologies, including those offering higher power density and larger capacities, are essential for the sustainable deployment of EVs.”
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- Premiseown data
The framework identifies three equitable BESS placement solutions involving eight mandatory busses.
“Key findings include identifying three equitable BESS placement solutions involving eight mandatory busses.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mohamad et al. (2021)
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AI techniques have expanded practical application of ESS in engineering applications.
“While AI techniques have expanded the practical application of ESS in engineering applications”
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The integration of V2Gs into existing power grids remains underdeveloped, requiring further research and optimization.
“However, the integration of V2Gs into existing power grids remains underdeveloped, requiring further research and optimization.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ahmadi et al. (2019)
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Strategic placement and capacity allocation further reduced ESEC by approximately 27% across all solutions.
“This strategic placement and capacity allocation further reduced ESEC by approximately 27% across all solutions, demonstrating the framework's effectiveness over traditional methods.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mohamad et al. (2021)
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The combinatorial optimization framework by Mohamad et al. demonstrates notable efficiency gains when applied to the IEEE 24-bus RTN.
“The framework demonstrates notable efficiency gains when applied to the IEEE 24-bus RTN, which includes 38 transmission lines and multiple generator and load busses.”
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- Interpretationreasoning
Well-coordinated charging methods, particularly those that optimize charging times during low-demand periods, offer substantial benefits for grid stability and efficiency.
“It highlights that well-coordinated charging methods, particularly those that optimize charging times during low-demand periods, offer substantial benefits for grid stability and efficiency.”
Quote in the paperNot a substantial claim - Premisecitation
Widespread adoption of EVs introduces challenges, particularly in power quality within electrical grids, due to G2V and V2G capabilities.
“However, their widespread adoption introduces challenges, particularly in power quality (PQ) within electrical grids, due to their grid-to-vehicle (G2V) and V2G capabilities.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ahmadi et al. (2019)
Not checked in the free run.
- Interpretationreasoning
AI can optimize the location, size, and operation of BESSs, considering factors influencing battery lifespan and uncertainties in solar and wind power generation.
“AI can optimize the location, size, and operation of battery energy storage systems (BESSs), considering factors influencing battery lifespan and uncertainties in solar and wind power generation.”
Quote in the paperSubstantial claim - Premisecitation
AI-driven modeling techniques can help identify the most cost-effective alternatives for grid-integrated seasonal storage technologies such as pumped hydro, compressed air, and hydrogen.
“For grid-integrated seasonal storage techno-economic analyses, AI-driven modeling techniques can help identify the most cost-effective alternatives for storage technologies such as pumped hydro, compressed air, and hydrogen(Guerra et al., 2020).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Guerra et al., 2020
Not checked in the free run.
- Premisecitation
Their system's high cell-level accuracy and low false negatives significantly reduced time required to identify and correct problematic cells.
“Their system's high level of cell-level accuracy and low number of false negatives significantly reduced the amount of time required to identify and correct problematic cells.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ahan et al. (2021)
Not checked in the free run.
- Premisecitation
The development of EVs is closely tied to the establishment of private and public charging infrastructures.
“The development of EVs is closely tied to the establishment of private and public charging infrastructures.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ahmadi et al. (2019)
Not checked in the free run.
- Premisenone given
Varying C-rates and reliability levels of BESSs show an inverse relationship between ESEC and Expected Solar Energy Stored (ESES).
“Additionally, exploring varying C-rates and reliability levels of BESSs shows an inverse relationship between ESEC and the Expected Solar Energy Stored (ESES), adding another dimension to the framework's applicability.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mohamad et al. (2021)
Not checked in the free run.
- Premiseown data
Optimized placements show an average reduction in Energy Storage Capacity (ESEC) by 8.85% compared to conventional deployment strategies, even before capacity distribution optimization.
“Even before capacity distribution optimization, these optimized placements show an average reduction in Energy Storage Capacity (ESEC) by 8.85% compared to conventional deployment strategies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mohamad et al. (2021)
Not checked in the free run.
- Premisecitation
Optimizing artificial intelligence could improve solar tracking systems.
“Their findings demonstrated that optimizing artificial intelligence could improve solar tracking systems.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ali et al. (2021)
Not checked in the free run.
- Interpretationcitation
AI algorithms have improved performance and cost-effectiveness of photovoltaic systems.
“AI algorithms have improved the performance and cost-effectiveness of photovoltaic systems”
Quote in the paperSubstantial claim - Interpretationcitation
IoT has accelerated detection and repair of defective solar cells.
“the Internet of Things has accelerated the detection and repair of defective solar cells.”
Quote in the paperSubstantial claim - Interpretationreasoning
Deep learning models have accurately predicted solar energy generation.
“deep learning models have accurately predicted solar energy generation”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 46
- May combine two claims. 53
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenMachine learning can help in design, optimization, cost reduction, and improving the efficacy of solar and wind energy, including advancing energy storage.
“Machine learning can help in design, optimization, cost reduction, and, most importantly, in improving the efficacy of solar and wind energy, including advancing energy storage.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTo fulfill the world's energy needs sustainably by 2030, annual investment in renewable energy must exceed $1 trillion.
“To fulfill the world's energy needs sustainably by 2030, the International Energy Agency (IEA) estimates that annual investment in renewable energy must exceed $1 trillion (Gielen, 2017).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Gielen, 2017
Not checked in the free run.
May combine two claims.
PremisecitationRenewable energy sources face inherent challenges, such as intermittency, weather-dependent production, and high initial investment costs.
“Despite the considerable increase in the global installed capacity of solar photovoltaics and wind energy, renewable energy sources face inherent challenges, such as intermittency, weather-dependent production, and high initial investment costs (Obaideen et al., 2021).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Obaideen et al., 2021
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningMachine learning applications for solar and wind energy generation are vital for sustainable energy production.
“Machine learning applications for solar and wind energy generation are vital for sustainable energy production.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenThis review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.
“Ultimately, this review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenSolar panels can provide abundant, low-carbon energy, thereby fostering energy security, decreasing fossil fuel consumption and emissions, and meeting rising electricity demand.
“Solar panels can provide abundant, low-carbon energy, thereby fostering energy security, decreasing fossil fuel consumption and emissions, and meeting the rising electricity demand.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAccording to Ferreira et al. (2018), solar energy could produce 22% of the world's electricity by 2050, reducing carbon emissions.
“According to Ferreira et al. (2018), solar energy could produce 22% of the world's electricity by 2050, reducing carbon emissions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ferreira et al. (2018)
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSolar panels are considered hazardous waste due to their environmental impact and energy loss.
“As a result of their environmental impact and energy loss, solar panels are considered hazardous waste (Korniejenko et al., 2021).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Korniejenko et al., 2021
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationBy integrating AI into distant control systems and decentralized energy production, renewable energy can support post-pandemic recovery and contribute to developing a low-carbon economy.
“By integrating AI into distant control systems and decentralized energy production, renewable energy can support post-pandemic recovery and contribute to developing a low-carbon economy (Hosseini, 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Hosseini, 2020
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe key to circumventing obstacles to renewable energy integration is the development of innovative AI technologies and energy storage strategies and the harmonization of legislation and economic incentives.
“The key to circumventing these obstacles is the development of innovative AI technologies and energy storage strategies and the harmonization of legislation and economic incentives to promote sustainable energy transitions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationResearchers have created hybrid nanogenerators that combine TENG and EMG technologies for self-powered wind speed sensing and fuelling commercial solar cells.
“Researchers have created hybrid nanogenerators that combine TENG and EMG technologies for self-powered wind speed sensing and fuelling commercial solar cells (Guo et al., 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Guo et al., 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAs onshore wind and solar energy technologies become less expensive, a growing number of individuals and organizations are opting for renewable energy sources, which are more financially viable than other energy sources due to their low initial cost.
“Additionally, as onshore wind and solar energy technologies become less expensive, a growing number of individuals and organizations are opting for renewable energy sources, which are more financially viable than other energy sources due to their low initial cost (Holm, 2013).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Holm, 2013
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe viability of renewable energy as a sustainable recovery strategy post-COVID-19 has been highlighted by various studies.
“The viability of renewable energy as a sustainable recovery strategy post-COVID-19 has been highlighted by various studies (Hosseini, 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Hosseini, 2020
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTriboelectric nanogenerators (TENGs) have emerged as promising devices that can capture a variety of mechanical energies, including wind energy.
“Triboelectric nanogenerators (TENGs) have emerged as promising devices that can capture a variety of mechanical energies, including wind energy.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Qian & Jing, 2018
Not checked in the free run.
May combine two claims.
PremisecitationEnhancement of FPV plants' capacity, ranging from 52 kW to 2 MW, can significantly reduce the levelized cost of energy (LCOE) by up to 85%, with further research potentially making FPV technology more affordable.
“The assertion is that the enhancement of FPV plants' capacity, ranging from 52 kW to 2 MW, can significantly reduce the levelized cost of energy (LCOE) by up to 85%, with further research, developments, and advancements in technology and materials potentially making FPV technology more affordable in the future (Gorjian et al., 2021).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Gorjian et al., 2021
Not checked in the free run.
May combine two claims.
PremisecitationAI applications have proven their ability to improve wind farm management, decision-making, and offshore wind energy production.
“AI applications have proven their ability to improve wind farm management, decision-making, and offshore wind energy production (Mitchell et al., 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mitchell et al., 2022
Not checked in the free run.
May combine two claims.
PremisecitationSince 2009, the price of solar panels has decreased by 80%, while their efficacy has increased, resulting in lower electricity costs.
“Since 2009, the price of solar panels has decreased by 80%, while their efficacy has increased, resulting in lower electricity costs (Couture & Gagnon, 2010).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Couture & Gagnon, 2010
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenWind energy has the potential to generate pure, non-polluting electricity on a global scale and is a rapidly expanding energy source.
“Wind energy has the potential to generate pure, non-polluting electricity on a global scale and is a rapidly expanding energy source.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationDespite solar energy's potential, its contribution to global energy production is negligible relative to other energy sources.
“Despite solar energy's potential, its contribution to global energy production is negligible relative to other energy sources (Ahmadi et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ahmadi et al., 2018
Not checked in the free run.
May combine two claims.
InterpretationcitationIn solar energy systems, machine learning algorithms enhance solar panel performance, increase energy forecasting, and optimize energy storage systems.
“In solar energy systems, machine learning algorithms enhance solar panel performance, increase energy forecasting, and optimize energy storage systems.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenIncorporating renewable energy sources, especially solar and wind, into existing systems encounters obstacles, such as fluctuating petrol and oil prices, economic factors, and policy barriers.
“However, incorporating renewable energy sources, especially solar and wind, into existing systems encounters obstacles, such as fluctuating petrol and oil prices, economic factors, and policy barriers.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn 2016, solar energy accounted for 3.5% of Europe's total energy production.
“In 2016, solar energy accounted for only 3.5% of Europe's total energy production”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Elshafei et al., 2021
Not checked in the free run.
May combine two claims.
PremisecitationAI has emerged as a transformative force in the renewable energy sector, potentially revolutionizing solar and wind energy solutions by optimizing production, increasing environmental awareness, and enhancing management and distribution.
“Artificial intelligence (AI) has emerged as a transformative force in the renewable energy sector, potentially revolutionizing solar and wind energy solutions by optimizing production, increasing environmental awareness, and enhancing management and distribution (Belu, 2013;Jha et al., 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Belu, 2013
Not checked in the free run.
- cites Jha et al., 2017
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Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenWind energy has acquired prominence as a renewable energy source due to the need to reduce carbon emissions and rising electricity demand.
“Wind energy has acquired prominence as a renewable energy source due to the need to reduce carbon emissions and the rising electricity demand.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationIn 2017, solar and wind energy accounted for more than 50% of the global increase in electricity generation capacity, with solar installations surpassing the combined total of new fossil fuel and nuclear power capacities.
“In 2017, solar and wind energy accounted for more than 50% of the global increase in electricity generation capacity, with solar installations surpassing the combined total of new fossil fuel and nuclear power capacities (Burke et al., 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Burke et al., 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn 2016, combustion fuels accounted for 48.7% of Europe's total energy production.
“combustion fuels accounted for 48.7% (Elshafei et al., 2021).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Elshafei et al., 2021
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationConcentrated solar power systems' high costs and lack of adaptability have prevented their pervasive deployment in large-capacity networks.
“Although concentrated solar power systems such as parabolic troughs, solar towers, and dish/ engine power plants use solar radiation to generate electricity, their high costs and lack of adaptability have prevented their pervasive deployment in large-capacity networks (Islam et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Islam et al., 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA revolution in energy is required to attain net-zero emissions by 2050.
“A revolution in energy is required to attain net-zero emissions by 2050 (Chu & Meisen, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Chu & Meisen, 2011
Not checked in the free run.
May combine two claims.
PremisecitationFloating solar PV systems offer advantages over land-based systems, including reduced land use and enhanced efficiency due to the cooling effect of water.
“Floating solar PV systems offer advantages over land-based systems, including reduced land use and enhanced efficiency due to the cooling effect of water (Mamatha & Kulkarni, 2022;Manoj Kumar et al., 2022).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Mamatha & Kulkarni, 2022
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- cites Manoj Kumar et al., 2022
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Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningSolar energy has the potential to significantly advance sustainable development goals.
“As one of the most abundant and purest renewable energy sources, solar energy has the potential to significantly advance sustainable development goals.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPerovskite solar cells have a simpler and less expensive production method than silicon solar panels.
“Perovskite solar cells, for example, have a simpler and less expensive production method than silicon solar panels (Wang et al., 2021).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wang et al., 2021
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTENGs offer benefits such as high voltage, low weight, and simple production, but their output is still modest.
“TENGs offer benefits such as high voltage, low weight, and simple production, but their output is still modest (Qian & Jing, 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Qian & Jing, 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenAchieving net-zero emissions by 2050 will necessitate a vast transition to renewable energy, with solar power playing a crucial role.
“Achieving net-zero emissions by 2050 will necessitate a vast transition to renewable energy, with solar power playing a crucial role in achieving this objective.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationEMGs have disadvantages, such as high maintenance costs and limited voltage output, which necessitate the development of alternative energy conversion technologies.
“Nevertheless, EMGs have disadvantages, such as high maintenance costs and limited voltage output, which necessitate the development of alternative energy conversion technologies (Ellabban et al., 2014).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ellabban et al., 2014
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PremisecitationDaily mean MAPE, NMAE, and FS values achieved are 4.67%, 0.82%, and 56.22%.
“Daily mean MAPE, NMAE, and FS values achieved are 4.67%, 0.82%, and 56.22%.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ma & Zhai, 2019
Not checked in the free run.
May combine two claims.
PremisecitationThe proposed multistep CNN stacked LSTM model outperformed CNN and LSTM models in both solar irradiance and POA irradiance prediction.
“The proposed multistep CNN stacked LSTM model outperformed CNN and -LSTM models in both solar irradiance and POA irradiance prediction.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zeng & Qiao, 2013
Not checked in the free run.
May combine two claims.
Premisenone givenThe multi-agent cooperation algorithm reduced the uncomfortable duration by 84%, the unconsumed amount of renewable energy by 43%, and the energy cost by 8% compared to the conventional rule-based control approach.
“Multi-agent cooperation algorithm reduced the uncomfortable duration by 84%, the unconsumed amount of renewable energy by 43%, and the energy cost by 8% compared to the conventional rule-based control approach.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe available roof area for PV installations in urban areas is 328 km2, and 81% of the total ground floor area of each building can be used as the available roof area for PV installation.
“The available roof area for PV installations in urban areas is 328 km2, and 81% of the total ground floor area of each building can be used as the available roof area for PV installation.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Assouline et al., 2017
Not checked in the free run.
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PremisecitationFlat and shed roofs had the highest received mean solar radiation (809 kWh/m2), followed by cross/corner (806 kWh/m2) and hip (800 kWh/m2).
“Flat & shed roofs had the highest received mean solar radiation (809 kWh/m2), followed by cross/corner (806 kWh/m2) and hip (800 kWh/m2).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mohajeri et al., 2018
Not checked in the free run.
May combine two claims.
PremisecitationANN had the best performance with the highest R2, lowest MAE, and lowest RMSE for wind energy production prediction, but had a longer training time and required more computational power.
“ANN had the best performance with the highest R2, lowest MAE, and lowest RMSE, but had a longer training time and required more computational power.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Buturache & Stancu, 2021
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PremisecitationThe WT-ACO-FFANN ML method improves 24-h ahead wind power forecast and outperforms other four evaluated methods (persistence, dual-step BP-FFANN, dual-step GA-FFANN, and dual-step PSO-FFANN) in MAPE, NMAE, variance, and FS.
“The WT-ACO-FFANN ML method improves 24-h ahead wind power forecast and outperforms other four evaluated methods (persistence, dual-step BP-FFANN, dual-step GA-FFANN, and dual-step PSO-FFANN) in MAPE, NMAE, variance, and FS.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ma & Zhai, 2019
Not checked in the free run.
May combine two claims.
PremisecitationV2G enhances grid functionality, including active power regulation, reactive power support, load balancing, and harmonic filtering.
“It highlights the benefits of V2G, such as active power regulation, reactive power support, load balancing, and current harmonic filtering.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mastoi et al., 2023
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenRenewable energy, such as solar and wind energy sources, has enormous potential to assist in achieving sustainable development goals and mitigating the effects of climate change.
“Renewable energy, such as solar and wind energy sources, has enormous potential to assist in achieving sustainable development goals and mitigating the effects of climate change.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTurbines cause noise pollution, which can be categorized into aerodynamic and mechanical noise, and this can be mitigated through improved design.
“(Nazir et al., 2020) suggest that turbines cause noise pollution, which can be categorized into aerodynamic and mechanical noise, and this can be mitigated through improved design.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Nazir et al., 2020
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May combine two claims.
PremisecitationV2G-equipped vehicles offer active and reactive power regulation, load balancing, and harmonics filtering functionalities.
“The work highlights that V2G-equipped vehicles offer active and reactive power regulation, load balancing, and harmonics filtering functionalities.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Habib et al. (2015)
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSolar energy can be utilized for desalination, heating, and photocatalysis.
“whereas solar energy can be utilized for desalination, heating, and photocatalysis.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Ding et al., 2021
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- cites Zhang et al., 2018
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenInnovative solutions like hybrid renewable energy systems, combining solar, wind, and bioenergy, can provide a more stable and continuous power supply.
“Innovative solutions like hybrid renewable energy systems, combining solar, wind, and bioenergy, can provide a more stable and continuous power supply.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationChallenges accompanying V2G advancements include potential battery life reduction, communication complexities, and necessary changes in distribution network infrastructure.
“However, challenges like reduced battery life, communication overhead between EVs and grids, and changes in distribution network infrastructure are also acknowledged.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mastoi et al., 2023
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAll energy sources contribute to pollution.
“According to Shahsavari and Akbari (2018), all energy sources contribute to pollution”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Shahsavari and Akbari (2018)
Not checked in the free run.
May combine two claims.
PremisecitationWind energy facilities have ecological implications such as noise pollution, visual disturbances, soil erosion, and deforestation.
“The paper identifies various ecological implications of wind energy facilities, such as noise pollution, visual disturbances, soil erosion, and deforestation, and proposes potential solutions to these challenges.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Nazir et al., 2020
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAs the global population and economy continue to expand, energy demand is anticipated to increase significantly over the next several decades.
“As the global population and economy continue to expand, energy demand is anticipated to increase significantly over the next several decades (Kober et al., 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kober et al., 2020
Not checked in the free run.
May combine two claims.
PremisecitationV2G faces challenges such as battery degradation, communication complexities, and infrastructural modifications in DNs.
“Nonetheless, V2G faces challenges such as battery degradation, communication complexities, and infrastructural modifications in DNs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Habib et al. (2015)
Not checked in the free run.
May combine two claims.
PremisecitationLithium-ion batteries are the most prevalent energy storage technology, and their environmental impact is a cause for concern.
“While lithium-ion batteries are the most prevalent energy storage technology, their environmental impact is a cause for concern (Pellow et al., 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Pellow et al., 2020
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningAlthough obstacles such as cost, energy storage, and integration with existing energy networks still exist, solar and wind energy are expected to become more prevalent soon.
“Although obstacles such as cost, energy storage, and integration with existing energy networks still exist, solar and wind energy are expected to become more prevalent soon.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningMachine learning algorithms can predict energy output from solar and wind sources, optimizing grid management and reducing reliance on backup fossil fuel sources.
“Specifically, machine learning algorithms can predict energy output from solar and wind sources, optimizing grid management and reducing reliance on backup fossil fuel sources.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationWith an annual energy production of 3146 MWh, a capacity factor of 36%, and a cost of electricity production lower than the wholesale price during the feed-in tariff period, the study determined the project to be economically viable.
“With an annual energy production of 3146 MWh, a capacity factor of 36%, and a cost of electricity production lower than the wholesale price during the feed-in tariff period, the study determined the project to be economically viable.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Himri et al., 2020
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenRenewable energy sources, such as solar and wind, have the potential to offer clean and affordable energy to meet the world's energy requirements.
“Renewable energy sources, such as solar and wind, have the potential to offer clean and affordable energy to meet the world's energy requirements.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWithout considering the concrete foundation, 73% of a wind turbine can be recycled.
“Interestingly, without considering the concrete foundation, 73% of a wind turbine can be recycled (Tazi et al., 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Tazi et al., 2019
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenWith technological improvements, harvesting energy from solar and wind sources has become increasingly viable and economical.
“With technological improvements, harvesting energy from these sources has become increasingly viable and economical.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenAI can be crucial in optimizing renewable energy systems, enhancing efficiency, and facilitating their incorporation into the power infrastructure.
“Artificial intelligence can be crucial in optimizing renewable energy systems, enhancing efficiency, and facilitating their incorporation into the power infrastructure.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationV2G technology has the potential to improve grid reliability, efficiency, loss mitigation, and stability.
“The paper also discusses the potential of V2G technology to improve grid reliability, efficiency, loss mitigation, and stability.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Habib et al. (2015)
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May combine two claims.
InterpretationcitationMachine learning proves indispensable for processing massive data volumes, providing predictive capabilities, and facilitating informed decision-making, thereby optimizing the operation of the grid.
“Machine learning proves indispensable for processing massive data volumes, providing predictive capabilities, and facilitating informed decision-making, thereby optimizing the operation of the grid.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hossain et al., 2019
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Interpretationnone givenChallenges to solar and wind energy include the need for quicker capacity expansion, technological advancements, and overcoming the intermittent nature of these energy sources.
“Still, challenges are presented, such as the need for quicker capacity expansion, technological advancements, and overcoming the intermittent nature of these energy sources.”
Quote in the paperSubstantial claim- Rests on an assumption: The intermittent nature of solar and wind power is one of the primary obstacles to reaching the growing energy demand.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenIntegration of intermittent renewable energy sources into the power grid requires energy storage.
“Integration of intermittent renewable energy sources into the power grid requires energy storage.”
Quote in the paperNot a substantial claim- Rests on an assumption: The intermittent nature of solar and wind power is one of the primary obstacles to reaching the growing energy demand.
May combine two claims.
PremisecitationSolar and wind capacity have grown rapidly, but they must increase by a factor of seven between 2010 and 2050 if two-thirds of the world's energy consumption is to come from renewable sources.
“Solar and wind capacity have grown rapidly, but they must increase by a factor of seven between 2010 and 2050 if two-thirds of the world's energy consumption is to come from renewable sources (Lowe & Drummond, 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lowe & Drummond, 2022
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May combine two claims.
PremisecitationSmart charging and discharging can reduce daily electricity costs, voltage deviations, and transformer and line current surges, enhancing DN stability, efficiency, and reliability.
“It is noted that smart charging and discharging can reduce daily electricity costs, voltage deviations, and transformer and line current surges, thereby enhancing the DN's stability, efficiency, and reliability.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Habib et al. (2015)
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIntegrating solar and wind energy in smart building designs can lead to more energy-efficient urban environments.
“Integrating solar and wind energy in smart building designs can lead to more energy-efficient urban environments.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationWind power has a lower overall environmental footprint compared to conventional energy sources, but it also has potential negative impacts including noise pollution, visual esthetics, ecological disruption, electromagnetic interference, and potential local climate change.
“Dhar et al. (2020) confirm the lower overall environmental footprint of wind power compared to conventional energy sources but also highlight several potential negative impacts.These include noise pollution, visual esthetics, ecological disruption, electromagnetic interference, and potential local climate change.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Dhar et al., 2020
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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationShadow flicker, soil erosion, deforestation, and the wake effect of turbines are concerns.
“The paper also addresses the shadow flicker, soil erosion, deforestation, and the wake effect of turbines (Nazir et al., 2020).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Nazir et al., 2020
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PremisecitationDVR-based energy storage systems have potential for wind energy conversion and emphasize the vital role of energy storage in grid stability maintenance.
“The study demonstrates the potential of DVR-based energy storage systems for wind energy conversion and emphasizes the vital role of energy storage in grid stability maintenance.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Pattanaik et al. (2021)
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenIncorporating AI, deep learning, and IoT has significantly improved solar energy generation and technology.
“Incorporating artificial intelligence (AI), deep learning, and the Internet of Things (IoT) has significantly improved solar energy generation and technology, which is a promising area.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAI-assisted analysis can aid in calculating storage operation profiles, estimating cost-effectiveness of storage devices, and accounting for avoided power system costs.
“AI-assisted analysis can aid in calculating storage operation profiles, estimating the cost-effectiveness of storage devices, and accounting for avoided power system costs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Guerra et al., 2020
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PremisecitationHESS demonstrates a constant power dispatch to the utility grid, with fast-changing power components charged-discharged by SESS and slow-changing components by BESS.
“Using a combination of bidirectional power converters, a three-level T-type inverter, and an LCL filter, the research demonstrates a constant power dispatch to the utility grid, with fastchanging power components charged-discharged by short-term energy storage system (SESS) and slow-changing components by battery energy storage system (BESS).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Roy et al. (2020)
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PremisecitationAI algorithms can improve performance and reduce cost of photovoltaic (PV) systems, potentially reducing environmental impact of PV power systems.
“In a related study, Meena et al. (2022) utilized AI algorithms to improve the performance and reduce the cost of photovoltaic (PV) systems, indicating that AI-based solutions may be able to reduce the environmental impact of PV power systems.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Meena et al. (2022)
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
PremisereasoningA multi-layer, communication-enhanced infrastructure is necessary for coordinating between system operators, EV aggregators, and end-users.
“This necessitates a multi-layer, communication-enhanced infrastructure for coordinating between system operators, EV aggregators, and end-users.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ahmadi et al. (2019)
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PremisecitationThe DVR system is important in compensating for under voltage while maintaining grid stability.
“It highlights the importance of the DVR system in compensating for under voltage while maintaining grid stability, thereby highlighting the significance of energy storage in renewable energy systems”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Pattanaik et al. (2021)
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningThe integration of V2G technology with intelligent grid communication systems is crucial for the successful operation of this synergy.
“The integration of V2G technology with intelligent grid communication systems is crucial for the successful operation of this synergy.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationUsing Electroluminescence (EL) Imaging, Ahan et al. detected and localized issues with solar panels, enhancing performance and reducing warranty and service costs.
“Using Electroluminescence (EL) Imaging, they detected and localized issues with solar panels, thereby enhancing performance and reducing warranty and service costs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ahan et al. (2021)
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May combine two claims.
InterpretationreasoningAI optimization of BESS location, size, and operation reduces losses, enhances voltage regulation, and lengthens battery life.
“This strategy reduces losses, enhances voltage regulation, and lengthens battery life.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationSuccessful integration also hinges on developing cost-effective, multifunctional EV chargers capable of providing ancillary services such as voltage regulation and harmonic reduction in both G2V and V2G modes.
“It also hinges on developing cost-effective, multifunctional EV chargers capable of providing ancillary services, such as voltage regulation and harmonic reduction, in both G2V and V2G modes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ahmadi et al. (2019)
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May combine two claims.
PremisecitationSimulation outcomes included a compensated sag voltage of the grid terminal voltage, a regulated DC link voltage, and efficient bidirectional power flow in the energy storage battery system.
“Important outcomes of the simulation included a compensated sag voltage of the grid terminal voltage, a regulated DC link voltage, and an efficient bidirectional power flow in the energy storage battery system.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Pattanaik et al. (2021)
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Interpretationnone givenAI technologies have potential to significantly improve wind energy forecasts and wind speed predictions, leading to increased efficiency and cost reduction.
“Various research has highlighted the potential for AI technologies to significantly improve wind energy forecasts and wind speed predictions, leading to increased efficiency and cost reduction.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationAdditional research is needed into AI's potential in multi-objective control of power system optimization.
“additional research is still needed into AI's potential in multi-objective control of power system optimization.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Abdalla et al. (2021)
Not checked in the free run.
The paper relies on this without support.
Solar and wind energy are essential to achieving the Sustainable Development Goals.
“Solar and wind energy are essential to achieving these goals, as they are two of the most promising renewable energy sources.”
The paper relies on this without support.
The long-term benefits of renewable energy development typically outweigh the costs.
“However, the long-term benefits of renewable energy development, such as environmental protection, reduction of greenhouse gas emissions, and creation of new business opportunities, typically outweigh the costs.”
The paper relies on this without support.
The intermittent nature of solar and wind power is one of the primary obstacles to reaching the growing energy demand.
“The intermittent nature of solar and wind power is one of the primary obstacles to reaching the growing energy demand.”
The paper relies on this without support.
The intermittent nature of solar and wind energy poses a challenge to attaining a consistent power supply, making energy storage essential.
“The intermittent nature of solar and wind energy poses a challenge to attaining a consistent power supply, making energy storage essential.”
The paper relies on this without support.
Li-ion batteries can be recharged 2000-4000 times and cost $200-$500 per kWh.
“It is noted that Li-ion batteries can be recharged 2000-4000 times and cost $200-$500 per kWh.”
The paper relies on this without support.
The degradation cost of batteries at 80% discharge depth is approximately $130 per MWh for a 300-kWh investment.
“The degradation cost of batteries at 80% discharge depth is approximately $130 per MWh for a 300-kWh investment.”
The paper relies on this without support.
Smart metering and charging can increase self-consumption by 45%.
“It is revealed that smart metering and charging can increase self-consumption by 45%.”
The paper relies on this without support.
AI can optimize energy distribution based on predetermined criteria, resulting in more efficient use of renewable resources and reduced energy losses.
“Algorithms based on AI can model and optimize energy distribution based on predetermined criteria, resulting in more efficient use of renewable resources and reduced energy losses.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The interlocking climate and energy crises require an urgent transition to renewable energy for sustainability and emission reduction.
“These interlocking climate and energy crises require an urgent transition to renewable energy for sustainability and emission reduction [14, 154].”
From Biofuel production: exploring renewable energy solutions for a greener future (2024) · cites this paper as [154]
Fossil fuel reserves are finite and being rapidly depleted, with oil potentially peaking soon after.
“Fossil fuel reserves are finite and being rapidly depleted, with oil potentially peaking soon after [154].”
From Biofuel production: exploring renewable energy solutions for a greener future (2024) · cites this paper as [154]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Figure 4 is identified as both wind and solar applications
MinorThe text directs readers to Figure 4 for AI applications in wind energy, but the Figure 4 caption identifies it as solar energy production.
“These case studies underscore the enormous potential of AI to improve wind energy generation and technology, especially in its four main areas of application, as depicted in Figure 4.”
“F I G U R E 4The applications of artificial intelligence for solar energy production.”
Wind-energy table includes a photovoltaic-system study
MinorTable 2 is labeled as covering machine-learning applications to wind energy, but one of its entries concerns photovoltaic-system performance and data from a PV module.
“T A B L E 2 The various applications of machine learning to wind energy.”
“Support vector machine (SVM) classification Analysis and optimization of photovoltaic (PV) system performance Monitoring data of a 270 W mono-crystalline PV module over seven months”
Three-day simulation contains only one day of one-minute periods
MinorThree days contain 4,320 one-minute periods, not 1,440. The stated period count covers only one day.
“period of 3 days”
“divided into 1440 periods (1-min time steps)”
Cites
1 retracted
Dhanalakshmi, P., Venkatesh, V., Ranjit, P. S., et al. (2022) · International Journal of Photoenergy · doi:10.1155/2022/5756610 · Cited by 6 · see citing works
RetractedRenewable energy resources: current status, future prospects and their enabling technology
Ellabban, O., Abu-Rub, H., & Blaabjerg, F. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.07.113 · Cited by 3,134 · see citing works
Solar energy: Potential and future prospects
Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., & Kim, K. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.09.094 · Cited by 2,491 · see citing works
Solar energy for future world: A review
Kannan, N., & Vakeesan, D. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.05.022 · Cited by 2,104 · see citing works
Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply
Kabeyi, M. J. B., & Olanrewaju, O. A. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.743114 · Cited by 1,177 · see citing works
Potential of solar energy in developing countries for reducing energy-related emissions
Shahsavari, A., & Akbari, M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.065 · Cited by 1,045 · see citing works
Islam, M. T., Huda, N., Abdullah, A. B., et al. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.04.097 · Cited by 840 · see citing works
An analysis of feed-in tariff remuneration models: Implications for renewable energy investment
Couture, T. D., & Gagnon, Y. (2009) · Energy Policy · doi:10.1016/j.enpol.2009.10.047 · Cited by 801 · see citing works
Sustainable development using renewable energy technology
Østergaard, P. A., Duić, N., Noorollahi, Y., Mikulčić, H., & Kalogirou, S. A. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.08.094 · Cited by 740 · see citing works
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711 · see citing works
Solar energy technology and its roles in sustainable development
Maka, A. O. M., & Alabid, J. M. (2022) · Clean Energy · doi:10.1093/ce/zkac023 · Cited by 711 · see citing works
Cantarero, M. M. V. (2020) · Energy Research & Social Science · doi:10.1016/j.erss.2020.101716 · Cited by 707 · see citing works
World Development Indicators 2004
Bank, W. (2004) · World Bank, Washington, DC eBooks · doi:10.1596/0-8213-5729-8 · Cited by 686 · see citing works
Wind and solar energy curtailment: A review of international experience
Bird, L. A., Lew, D. J., Milligan, M. R., Carlini, E. M., Estanqueiro, A. I., Flynn, D., Gómez‐Lázaro, E., Holttinen, H., Menemenlis, N., Orths, A., et al. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.06.082 · Cited by 665 · see citing works
Hoang, A. T., Pham, V. V., & Nguyen, X. P. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127161 · Cited by 663 · see citing works
Solar energy-A look into power generation, challenges, and a solar-powered future
Hayat, M. B., Ali, D., Monyake, K. C., Alagha, L., & Ahmed, N. (2018) · International Journal of Energy Research · doi:10.1002/er.4252 · Cited by 581 · see citing works
Habib, S., Kamran, M., & Rashid, U. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.12.020 · Cited by 556 · see citing works
Wind energy: Trends and enabling technologies
Kumar, Y., Ringenberg, J., Depuru, S. S. S. R., Devabhaktuni, V., Lee, J. W., Nikolaidis, E., Andersen, B., & Afjeh, A. A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.07.200 · Cited by 546 · see citing works
Ding, T., Zhou, Y., Ong, W. L., & Ho, G. W. (2020) · Materials Today · doi:10.1016/j.mattod.2020.10.022 · Cited by 528 · see citing works
Hossain, E., Khan, I., Un-Noor, F., Sikander, S. S., & Sunny, M. S. H. (2019) · IEEE Access · doi:10.1109/access.2019.2894819 · Cited by 514 · see citing works
Renewable energy, non-renewable energy and sustainable development
Güney, T. (2019) · International Journal of Sustainable Development & World Ecology · doi:10.1080/13504509.2019.1595214 · Cited by 507 · see citing works
Wind power forecasting based on daily wind speed data using machine learning algorithms
Demolli, H., Dokuz, A. Ş., Ecemiş, A., & Gökçek, M. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111823 · Cited by 474 · see citing works
Global energy perspectives to 2060 – WEC's World Energy Scenarios 2019
Kober, T., Schiffer, H., Densing, M., et al. (2020) · Energy Strategy Reviews · doi:10.1016/j.esr.2020.100523 · Cited by 469 · see citing works
Abdalla, A. N., Nazir, M. S., Hai, T., Cao, S., Ji, R., Jiang, M., & Liu, Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102811 · Cited by 421 · see citing works
Solar power technology for electricity generation: A critical review
Ahmadi, M. H., Ghazvini, M., Sadeghzadeh, M., et al. (2018) · Energy Science & Engineering · doi:10.1002/ese3.239 · Cited by 410 · see citing works
How renewable energy alleviate energy poverty? A global analysis
Zhao, J., Dong, K. Y., Dong, X., & Shahbaz, M. Q. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.01.005 · Cited by 406 · see citing works
Gorjian, S., Sharon, H., Ebadi, H., Kant, K., Scavo, F. B., & Tina, G. M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124285 · Cited by 386 · see citing works
Renewable energy: Present research and future scope of Artificial Intelligence
Jha, S. K., Bilalovic, J., Jha, A., Patel, N. V., & Zhang, H. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.04.018 · Cited by 376 · see citing works
Short-term solar power prediction using a support vector machine
Zeng, J., & Qiao, W. (2012) · Renewable Energy · doi:10.1016/j.renene.2012.10.009 · Cited by 363 · see citing works
Kuzemko, C., Blondeel, M., Dupont, C., et al. (2022) · Energy Research & Social Science · doi:10.1016/j.erss.2022.102842 · Cited by 337 · see citing works
Wind energy developments and policies in China: A short review
Sahu, B. K. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.183 · Cited by 326 · see citing works
Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
Zahraee, S. M., Assadi, M. K., & Saidur, R. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.08.028 · Cited by 318 · see citing works
Hoppmann, J., Huenteler, J., & Girod, B. (2014) · Research Policy · doi:10.1016/j.respol.2014.01.014 · Cited by 313 · see citing works
A review of foundations of offshore wind energy convertors: Current status and future perspectives
Oh, K., Nam, W., Ryu, M. S., et al. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.02.005 · Cited by 310 · see citing works
An interdisciplinary review of energy storage for communities: Challenges and perspectives
Parra, D., Swierczynski, M., Stroe, D. I., Norman, S. A., Abdon, A., Worlitschek, J., O’Doherty, T., Rodrigues, L., Gillott, M., Zhang, X., et al. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.003 · Cited by 307 · see citing works
Application of solar energy in water treatment processes: A review
Zhang, Y., Sivakumar, M., Yang, S., Enever, K., & Ramezanianpour, M. (2017) · Desalination · doi:10.1016/j.desal.2017.11.020 · Cited by 303 · see citing works
Obaideen, K., AlMallahi, M. N., Alami, A. H., Ramadan, M., Abdelkareem, M. A., Shehata, N., & Olabi, A. G. (2021) · International Journal of Thermofluids · doi:10.1016/j.ijft.2021.100123 · Cited by 298 · see citing works
The value of seasonal energy storage technologies for the integration of wind and solar power
Guerra, O. J., Zhang, J., Eichman, J., Denholm, P. L., Kurtz, J., & Hodge, B. S. (2020) · Energy & Environmental Science · doi:10.1039/d0ee00771d · Cited by 297 · see citing works
An outlook on the global development of renewable and sustainable energy at the time of COVID-19
Hosseini, S. E. (2020) · Energy Research & Social Science · doi:10.1016/j.erss.2020.101633 · Cited by 297 · see citing works
Wind power generation: A review and a research agenda
Aguilar, S., Esteves, G. R. T., Maçaira, P., Bastos, B. Q., Oliveira, F. L. C., & Souza, R. C. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.02.015 · Cited by 296 · see citing works
Rangel-Martínez, D., Nigam, K. D. P., & Ricardez‐Sandoval, L. (2021) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2021.08.013 · Cited by 265 · see citing works
Quantifying rooftop photovoltaic solar energy potential: A machine learning approach
Assouline, D., Mohajeri, N., & Scartezzini, J. (2016) · Solar Energy · doi:10.1016/j.solener.2016.11.045 · Cited by 257 · see citing works
Potential assessment of photovoltaic power generation in China
Qiu, T., Wang, L., Lu, Y., Zhang, M., Qin, W., Wang, S., & Wang, L. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111900 · Cited by 246 · see citing works
Cui, L., Yue, S., Nghiem, X., et al. (2023) · Resources Policy · doi:10.1016/j.resourpol.2023.103373 · Cited by 246 · see citing works
Integrating renewable energy in smart grid system: Architecture, virtualization and analysis
Worighi, I., Maach, A., Hafid, A. S., Hegazy, O., & Mierlo, J. V. (2019) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2019.100226 · Cited by 243 · see citing works
A Comprehensive Review on Smart Grids: Challenges and Opportunities
Escobar, J. J. M., Matamoros, O. M., Padilla, R. T., Reyes, I. L., & Espinosa, H. Q. (2021) · Sensors · doi:10.3390/s21216978 · Cited by 242 · see citing works
Dhar, A., Naeth, M. A., Jennings, P. D., & El‐Din, M. G. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134602 · Cited by 240 · see citing works
Economic overview of the use and production of photovoltaic solar energy in brazil
Ferreira, A., Kunh, S. S., Fagnani, K. C., et al. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.06.102 · Cited by 234 · see citing works
Hassan, S. T., Batool, B., Zhu, B., & Khan, I. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.130735 · Cited by 232 · see citing works
Energy poverty in developing countries: A review of the concept and its measurements
Sy, S. A., & Mokaddem, L. (2022) · Energy Research & Social Science · doi:10.1016/j.erss.2022.102562 · Cited by 230 · see citing works
Urban wind energy: Some views on potential and challenges
Stathopoulos, T., Alrawashdeh, H., Al‐Quraan, A., et al. (2018) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2018.05.018 · Cited by 228 · see citing works
Overcoming barriers to solar and wind energy adoption in two Asian giants: India and Indonesia
Burke, P. J., Widnyana, J., Anjum, Z., Aisbett, E., Resosudarmo, B. P., & Baldwin, K. G. H. (2019) · Energy Policy · doi:10.1016/j.enpol.2019.05.055 · Cited by 222 · see citing works
Assessment of wind energy potential using wind energy conversion system
Shoaib, M., Siddiqui, I. A., Rehman, S., Khan, S., & Alhems, L. M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.01.128 · Cited by 218 · see citing works
Optimum allocation of battery energy storage systems for power grid enhanced with solar energy
Mohamad, F., Teh, J., & Lai, C. (2021) · Energy · doi:10.1016/j.energy.2021.120105 · Cited by 215 · see citing works
Challenges and opportunities towards the development of floating photovoltaic systems
Kumar, M., Niyaz, H. M., & Gupta, R. (2021) · Solar Energy Materials and Solar Cells · doi:10.1016/j.solmat.2021.111408 · Cited by 214 · see citing works
Mastoi, M. S., Zhuang, S., Munir, H. M., Haris, M., Hassan, M., Alqarni, M., & Alamri, B. R. (2023) · Energy Reports · doi:10.1016/j.egyr.2022.12.139 · Cited by 197 · see citing works
Djellouli, N., Abdelli, L., Elheddad, M., Ahmed, R., & Mahmood, H. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.10.066 · Cited by 191 · see citing works
Potential environmental impacts of wind energy development: A global perspective
Nazir, M. S., Ali, N., Bilal, M., et al. (2020) · Current Opinion in Environmental Science & Health · doi:10.1016/j.coesh.2020.01.002 · Cited by 190 · see citing works
The role of machine learning to boost the bioenergy and biofuels conversion
Wang, Z., Peng, X., Xia, A., Shah, A. A., Huang, Y. Y., Zhu, X., Zhu, X., & Liao, Q. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126099 · Cited by 173 · see citing works
Zhao, E., Sun, S., & Wang, S. (2022) · Data Science and Management · doi:10.1016/j.dsm.2022.05.002 · Cited by 171 · see citing works
Mehmood, K. K., Khan, S. U., Lee, S., et al. (2017) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2016.0938 · Cited by 163 · see citing works
A systematic review of environmental and economic impacts of smart grids
Moretti, M., Djomo, S. N., Azadi, H., May, K., Vos, K. D., Passel, S. V., & Witters, N. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.03.039 · Cited by 156 · see citing works
Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model
Alali, E., Hajji, Y., Said, Y., et al. (2023) · Mathematics · doi:10.3390/math11030676 · Cited by 156 · see citing works
Pellow, M. A., Ambrose, H., Mulvaney, D., Betita, R., & Shaw, S. (2020) · Sustainable Materials and Technologies · doi:10.1016/j.susmat.2019.e00120 · Cited by 152 · see citing works
Solar, wind and logistic substitution in global energy supply to 2050 – Barriers and implications
Lowe, R., & Drummond, P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111720 · Cited by 151 · see citing works
Towards a new renewable power system using energy storage: An economic and social analysis
Sánchez, A., Zhang, Q., Martı́n, M., & Vega, P. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.115056 · Cited by 148 · see citing works
Shen, R., Zhong, S., Wen, X., An, Q., Zheng, R., Yang, L., & Zhao, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118724 · Cited by 144 · see citing works
A city-scale roof shape classification using machine learning for solar energy applications
Mohajeri, N., Assouline, D., Guiboud, B., et al. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.12.096 · Cited by 143 · see citing works
Qian, J., & Jing, X. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.07.035 · Cited by 143 · see citing works
Solarin, S. A., Al‐mulali, U., Goh, G. G. G., & Shahbaz, M. Q. (2018) · Environmental Science and Pollution Research · doi:10.1007/s11356-018-2392-5 · Cited by 142 · see citing works
Wu, Y., Lee, C.-Y., Chen, C.-R., et al. (2013) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2013.2283219 · Cited by 138 · see citing works
Richards, G. W., Noble, B. F., & Belcher, K. W. (2012) · Energy Policy · doi:10.1016/j.enpol.2011.12.049 · Cited by 133 · see citing works
A comprehensive review of automatic cleaning systems of solar panels
Derakhshandeh, J. F., AlLuqman, R., Mohammad, S., et al. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101518 · Cited by 129 · see citing works
Impacts of Renewable Energy Resources in Smart Grid
Ayadi, F., Çolak, İ., Garip, İ., et al. (2020) · doi:10.1109/icsmartgrid49881.2020.9144695 · Cited by 121 · see citing works
Wang, Q., & Zhan, L. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.07.170 · Cited by 119 · see citing works
Migrating from traditional grid to smart grid in smart cities promoted in developing country
Neffati, O. S., Sengan, S., Thangavelu, K. D., Kumar, S. D., Setiawan, R., Elangovan, M., Mani, D., & Velayutham, P. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101125 · Cited by 114 · see citing works
Mitchell, D., Blanche, J., Harper, S., Lim, T., Gupta, R., Zaki, O., Tang, W., Robu, V., Watson, S., & Flynn, D. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100146 · Cited by 114 · see citing works
Targeting climate adaptation to safeguard and advance the Sustainable Development Goals
Fuldauer, L. I., Thacker, S., Haggis, R. A., Nerini, F. F., Nicholls, R. J., & Hall, J. W. (2022) · Nature Communications · doi:10.1038/s41467-022-31202-w · Cited by 113 · see citing works
Economic feasibility of floating offshore wind farms in Portugal
SANTOS, L. C., Silva, D., Bento, A. R., et al. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107393 · Cited by 112 · see citing works
Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique
Michael, N. E., Mishra, M., Hasan, S., et al. (2022) · Energies · doi:10.3390/en15062150 · Cited by 108 · see citing works
Forecasting Solar Energy Production Using Machine Learning
Vennila, C., Titus, A., Sudha, T. S., et al. (2022) · International Journal of Photoenergy · doi:10.1155/2022/7797488 · Cited by 105 · see citing works
A Comprehensive Review of Floating Solar Plants and Potentials for Offshore Applications
Huang, G., Tang, Y., Chen, X., Chen, M., & Jiang, Y. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11112064 · Cited by 105 · see citing works
Movsessian, A., Cava, D. G., & Tcherniak, D. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107766 · Cited by 103 · see citing works
Korniejenko, K., Kozub, B., Bąk, A., Balamurugan, P., Uthayakumar, M., & Furtos, G. (2021) · Journal of Composites Science · doi:10.3390/jcs5090243 · Cited by 99 · see citing works
Technical and economic analysis of wind energy potential in Uzbekistan
Bahrami, A., Teimourian, A., Okoye, C. O., et al. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.03.140 · Cited by 97 · see citing works
Power quality improvement in smart grids using electric vehicles: a review
Ahmadi, A., Tavakoli, A., Jamborsalamati, P., Rezaei, N., Miveh, M. R., Gandoman, F. H., Heidari, A., & Nezhad, A. E. (2019) · IET Electrical Systems in Transportation · doi:10.1049/iet-est.2018.5023 · Cited by 96 · see citing works
Waste and material flow analysis in the end-of-life wind energy system
Tazi, N., Kim, J., Bouzidi, Y., et al. (2019) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2019.02.039 · Cited by 95 · see citing works
Pata, U. K., Yılancı, V., Zhang, Q., et al. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.07.008 · Cited by 95 · see citing works
Guo, Y., Chen, Y., Ma, J., et al. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.03.094 · Cited by 90 · see citing works
Balsalobre‐Lorente, D., Sinha, A., & Murshed, M. (2023) · Energy Economics · doi:10.1016/j.eneco.2023.106847 · Cited by 81 · see citing works
Recent Trends on Liquid Air Energy Storage: A Bibliometric Analysis
Borri, E., Tafone, A., Zsembinszki, G., Comodi, G., Romagnoli, A., & Cabeza, L. F. (2020) · Applied Sciences · doi:10.3390/app10082773 · Cited by 73 · see citing works
Potential and economic feasibility of wind energy in south West region of Algeria
Himri, Y., Merzouk, M., Merzouk, N. K., et al. (2020) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2020.100643 · Cited by 72 · see citing works
Nobre, R. L. G., Boulêtreau, S., Colas, F., et al. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113852 · Cited by 72 · see citing works
Financing for renewable energy projects: A decision guide by developmental stages with case studies
Lam, P. T. I., & Law, A. O. K. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.083 · Cited by 69 · see citing works
Roy, P. K. S., He, J., & Liao, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3037149 · Cited by 69 · see citing works
Naganathan, H., Chong, W. O., & Chen, X. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.08.002 · Cited by 68 · see citing works
Wind energy in Malaysia: Past, present and future
Ho, L.-W. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.08.054 · Cited by 67 · see citing works
Kumar, N. M., Chakraborty, S., Yadav, S. K., et al. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102168 · Cited by 66 · see citing works
Transformation of Smart Grid using Machine Learning
Azad, S., Sabrina, F., & Wasimi, S. A. (2019) · Australasian Universities Power Engineering Conference · doi:10.1109/aupec48547.2019.211809 · Cited by 59 · see citing works
Zhao, C., Wu, Y., Dai, X., et al. (2022) · Materials & Design · doi:10.1016/j.matdes.2022.111005 · Cited by 56 · see citing works
Overview of the Policy Instruments for Renewable Energy Development in China
Song, D., Liu, Y., Qin, T., et al. (2022) · Energies · doi:10.3390/en15186513 · Cited by 55 · see citing works
Critical aspects of wind energy systems in smart grid applications
Çolak, İ., Fulli, G., Bayhan, S., Chondrogiannis, S., & Demirbaş, Ş. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.07.062 · Cited by 54 · see citing works
Public receptiveness of vertical axis wind turbines
Hui, I. S., Cain, B. E., & Dabiri, J. O. (2017) · Energy Policy · doi:10.1016/j.enpol.2017.10.028 · Cited by 54 · see citing works
Feasibility of offshore wind energy as an alternative source for the state of Kuwait
Al-Nassar, W. K., Neelamani, S., Al-Salem, K., et al. (2018) · Energy · doi:10.1016/j.energy.2018.11.140 · Cited by 53 · see citing works
Wind Energy Prediction Using Machine Learning
Buturache, A.-N., & Stancu, S. (2021) · Low Carbon Economy · doi:10.4236/lce.2021.121001 · Cited by 53 · see citing works
Assessment of floating solar photovoltaic potential in India’s existing hydropower reservoirs
Mamatha, G., & Kulkarni, P. S. (2022) · Energy Sustainable Development/Energy for sustainable development · doi:10.1016/j.esd.2022.05.011 · Cited by 52 · see citing works
Rokicki, T., Bórawski, P., & Szeberényi, A. (2023) · Energies · doi:10.3390/en16186629 · Cited by 52 · see citing works
Solar energy harvesting for smart farming using nanomaterial and machine learning
Vatti, R., Vatti, N., Mahender, K., Lakshmi Vatti, P., & Krishnaveni, B. (2020) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/981/3/032009 · Cited by 49 · see citing works
Exploration of Wind Energy in India: A Short Review
Sitharthan, R., Swaminathan, J. N., & Parthasarathy, T. (2018) · doi:10.1109/npec.2018.8476733 · Cited by 46 · see citing works
Taghinezhad, J., & Sheidaei, S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.02.065 · Cited by 45 · see citing works
Nogueira, T. D. O., Palacio, G. B. A., Braga, F. D., et al. (2021) · Energy · doi:10.1016/j.energy.2021.122064 · Cited by 44 · see citing works
A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models
Nezhad, M. M., Heydari, A., Neshat, M., Keynia, F., Piras, G., & Garcia, D. A. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.03.110 · Cited by 43 · see citing works
AI-based solar energy forecasting for smart grid integration
Said, Y., & Alanazi, A. M. M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08160-x · Cited by 43 · see citing works
Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning
Rushdi, M. A., Rushdi, A., Dief, T. N., Halawa, A. M., Yoshida, S., & Schmehl, R. (2020) · Energies · doi:10.3390/en13092367 · Cited by 40 · see citing works
Smart Integrated Renewable Energy Systems (SIRES): A Novel Approach for Sustainable Development
Maheshwari, Z., & Ramakumar, R. G. (2017) · Energies · doi:10.3390/en10081145 · Cited by 39 · see citing works
Ma, Y., & Zhai, M. (2019) · Applied Sciences · doi:10.3390/app9102125 · Cited by 37 · see citing works
Proof of Work Consensus Based Peer to Peer Energy Trading in the Indian Residential Community
Saini, V. K., Purohit, C. S., Kumar, R., et al. (2023) · Energies · doi:10.3390/en16031253 · Cited by 37 · see citing works
Milin, I. A., Mungiu-Pupăzan, M. C., Rehman, A., et al. (2022) · Sustainability · doi:10.3390/su14138125 · Cited by 35 · see citing works
Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)
Khabbouchi, I., Said, D., Oukaira, A., et al. (2023) · Energies · doi:10.3390/en16052179 · Cited by 35 · see citing works
Recent Developments and Future Advancements in Solar Panels Technology
Durganjali, C. S., Bethanabhotla, S., Kasina, S., et al. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1495/1/012018 · Cited by 34 · see citing works
Study of Massive Floating Solar Panels over Lake Nasser
Elshafei, M., Ibrahim, A. M., Helmy, A. S., et al. (2021) · Journal of Energy · doi:10.1155/2021/6674091 · Cited by 34 · see citing works
Role of Capital Market to Accelerate the Transition to Low-Carbon Energy System
Tian, H. (2018) · Springer eBooks · doi:10.1007/978-981-10-8582-6_9 · Cited by 28 · see citing works
Fang, J., Hu, W., Liu, Z., et al. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112788 · Cited by 27 · see citing works
Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications
Belu, R. G. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-1996-8.ch015 · Cited by 26 · see citing works
Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach
Staid, A., & Guikema, S. D. (2015) · Risk Analysis · doi:10.1111/risa.12324 · Cited by 25 · see citing works
Operational Data Analysis of a Battery Energy Storage System to Support Wind Energy Generation
Pontes, L., Costa, T., Souza, A. C. M., et al. (2023) · Energies · doi:10.3390/en16031468 · Cited by 25 · see citing works
Hassan, Q., Sameen, A. Z., Salman, H. M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2970234/v1 · Cited by 24 · see citing works
Analyzing the performance of photovoltaic systems using support vector machine classifier
Hafdaoui, H., Boudjelthia, E. A. K., Chahtou, A., et al. (2021) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2021.100592 · Cited by 23 · see citing works
Arnaoutakis, G. E., Kocher‐Oberlehner, G., & Katsaprakakis, D. A. (2023) · Mathematics · doi:10.3390/math11020391 · Cited by 20 · see citing works
Radtke, J., & Wurster, S. (2023) · Zeitschrift für Politikwissenschaft · doi:10.1007/s41358-023-00353-7 · Cited by 19 · see citing works
A Control Method using Artificial Intelligence in Wind Energy Conversion System
Arama, F. Z., Laribi, S., & Ghaitaoui, T. (2019) · Algerian Journal of Renewable Energy and Sustainable Development · doi:10.46657/ajresd.2019.1.1.6 · Cited by 16 · see citing works
Renewable Energy Future for the Developing World
Holm, D. (2013) · doi:10.1002/9783527673872.ch9 · Cited by 16 · see citing works
Artificial Intelligence Control Applied in Wind Energy Conversion System
Zohra, A. F., Bousserhane, I. K., Slimane, L., et al. (2018) · International Journal of Power Electronics and Drive Systems (IJPEDS) · doi:10.11591/ijpeds.v9.i2.pp571-578 · Cited by 14 · see citing works
A Brief Review on Smart Grid Residential Network Schemes
Fatima, N., Qasuria, T. A., & Ibrahim, M. A. (2020) · Sains Malaysiana · doi:10.17576/jsm-2020-4912-10 · Cited by 13 · see citing works
AI-assisted Cell-Level Fault Detection and Localization in Solar PV Electroluminescence Images
Ahan, M. R., Nambi, A. U., Ganu, T., Nahata, D., & Shivkumar Kalyanaraman, A. (2021) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3485730.3493455 · Cited by 13 · see citing works
Machine Learning Approach on Time Series for PV-Solar Energy
Sivakumar, S., Neeraja, B., Rani, M. J., et al. (2022) · Advances in Materials Science and Engineering · doi:10.1155/2022/6458377 · Cited by 13 · see citing works
Meena, R. S., Singh, A., Urhekar, S., et al. (2022) · International Journal of Photoenergy · doi:10.1155/2022/3437364 · Cited by 11 · see citing works
Solar Energy Optimisation Using IoT and Deep Learning- A Review
Partheeban, P., Shiva, M. Y., Vishnupriyan, J., et al. (2022) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai55433.2022.10028967 · Cited by 9 · see citing works
Energy Storage System with Dynamic Voltage Restorer Integrated For Wind Energy System
Pattanaik, B., Hussain, S. M., Karthikeyan, V., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1964/4/042098 · Cited by 8 · see citing works
Wang, H., Liu, G., Xu, C., et al. (2021) · Crystals · doi:10.3390/cryst11080933 · Cited by 8 · see citing works
Sustainable Energy Challenges in Realizing SDG 7
Dalei, N. N., Painuly, P. K., Rawat, A., & Heggde, G. S. (2021) · Encyclopedia of the UN sustainable development goals · doi:10.1007/978-3-319-71057-0_157-1 · Cited by 7 · see citing works
Ali, M., Firdaus, A. A., Nurohmah, H., et al. (2021) · 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) · doi:10.1109/iceeie52663.2021.9616931 · Cited by 5 · see citing works
Popkova, E. G., Bogoviz, A. V., & Sergi, B. S. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1145234 · Cited by 5 · see citing works
SunPower: A Path Toward Strategic Development
Singh, A. S. (2021) · Emerging Economies Cases Journal · doi:10.1177/25166042211058534 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Bank W., 2005, World development indicators 2005
- Chu Y., 2011, Review and comparison of different solar energy technologies, 1
- Gielen D., 2017, Perspectives for the energy transition investment needs for a low‐carbon energy system, International Renewable Energy Agency
- Van De Graaf T., 2023, The geopolitics of energy after Russia's war in Ukraine. Forging Europe's leadership: Global trends, Russian aggression and the risk of a regressive world Ukraine world
- Xue‐Song Z. Li‐Qiang C. &You‐Jie M.(2010).Research on smart grid technology. 2010 international conference on computer application and system modeling (ICCASM 2010). IEEE V3‐599‐V3‐603.
Cited by
Sorted by citations
Biofuel production: exploring renewable energy solutions for a greener future
El-Araby, R. (2024) · Biotechnology for Biofuels and Bioproducts · doi:10.1186/s13068-024-02571-9 · Cited by 276 · see citing works
- Authors
- Bin Abu Sofian, A. D. A., Lim, H. R., Siti Halimatul Munawaroh, H., Ma, Z., Chew, K. W., & Show, P. L.
- Affiliations
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering University of Nottingham Malaysia Semenyih Malaysia
- University of Nottingham (GB)
- Study Program of Chemistry, Faculty of Mathematics and Science Education Universitas Pendidikan Indonesia Bandung Indonesia
- Wenzhou University (CN)
- Nanyang Technological University (SG)
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Ma, Zengling
- Chew, Kit Wayne
- Show, Pau Loke
- Venue
- Sustainable Development
- Publisher
- Wiley
- Published
- 2024-01-08
- Volume
- 32
- Issue
- 4
- Pages
- 3953-3978
- ISSN
- 0968-0802 (ISSN-L)
- 1099-1719
- OpenAlex
- W4390675710
- Semantic Scholar
- 3a07972cb00d6da5c8641cee5e0266d86ee031ca
- Cited by
- 270
- References
- 144
- Citations by year
- 2026: 115 · 2025: 125 · 2024: 30
- FWCI
- 21.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Sustainable Development (publishedVersion, cc-by)
- Greater South Information System
- Funders
- Khalifa University of Science, Technology and Research (8474000580)
- Topics
- Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable energy and sustainable power systems
- Keywords
- solar energy, wind energy, renewable energy adoption, floating photovoltaics, vertical axis wind turbine, smart grid, energy storage, electric vehicles, machine learning applications, renewable energy economics, environmental impacts of renewable energy, global energy demand, sustainable energy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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