Machine learning for solar irradiance forecasting of photovoltaic system

A tutorial on hidden Markov models and selected applications in speech recognition

Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743 · see citing works

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,384 · see citing works

An introduction to genetic algorithms

Deb, K. (1999) · Sadhana · doi:10.1007/bf02823145 · Cited by 1,180 · see citing works

Artificial intelligence techniques for photovoltaic applications: A review

Mellit, A., & Kalogirou, S. (2008) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2008.01.001 · Cited by 1,014 · see citing works

A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

Mellit, A., & Pavan, A. M. (2010) · Solar Energy · doi:10.1016/j.solener.2010.02.006 · Cited by 895 · see citing works

Review of solar irradiance forecasting methods and a proposition for small-scale insular grids

Diagne, M., David, M., Lauret, P., Boland, J. W., & Schmutz, N. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.06.042 · Cited by 808 · see citing works

Elements of the theory of Markov processes and their applications

(1960) · Journal of the Franklin Institute · doi:10.1016/0016-0032(60)90686-4 · Cited by 734 · see citing works

AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS

Zoubin Ghahramani, A. (2001) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001401000836 · Cited by 701 · see citing works

Rainfall forecasting in space and time using a neural network

French, M. N., Krajewski, W. F., & Cuykendall, R. (1992) · Journal of Hydrology · doi:10.1016/0022-1694(92)90046-x · Cited by 700 · see citing works

Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

Sfetsos, A., & Coonick, A. H. (2000) · Solar Energy · doi:10.1016/s0038-092x(99)00064-x · Cited by 376 · see citing works

Elements of the Theory of Markov Processes and their Applications.

Morán, P. A. P., & Bharucha-Reid, A. T. (1961) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2343157 · Cited by 349 · see citing works

Solar Power Plants

(1991) · Springer eBooks · doi:10.1007/978-3-642-61245-9 · Cited by 260 · see citing works

A review on integration of artificial intelligence into water quality modelling

Chau, K. (2006) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2006.04.003 · Cited by 242 · see citing works

Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications

Palit, A. K., & Popović, D. (2005) · CERN Document Server (European Organization for Nuclear Research) · doi:10.1007/1-84628-184-9 · Cited by 239 · see citing works

Introduction to Genetic Algorithms

Sivanandam, S. N., & Deepa, S. N. (2007) · Springer eBooks · doi:10.1007/978-3-540-73190-0 · Cited by 170 · see citing works

Artificial neural network model for prediction solar radiation data: application for sizing stand-alone photovoltaic power system

Mellit, A., Benghanem, M., & Bendekhis, M. (2005) · IEEE Power Engineering Society General Meeting · doi:10.1109/pes.2005.1489526 · Cited by 120 · see citing works

DAILY INSOLATION FORECASTING USING A MULTI-STAGE NEURAL NETWORK

Kemmoku, Y., Orita, S., Nakagawa, S., & Sakakibara, T. (1999) · Solar Energy · doi:10.1016/s0038-092x(99)00017-1 · Cited by 120 · see citing works

Adaptive HVAC zone modeling for sustainable buildings

Platt, G., Li, J., Li, R., Poulton, G., James, G., & Wall, J. (2009) · Energy and Buildings · doi:10.1016/j.enbuild.2009.10.009 · Cited by 108 · see citing works

The total solar radiation time series simulation in Athens, using neural networks

Mihalakakou, G. K., Santamouris, M., & Asimakopoulos, D. N. (2000) · Theoretical and Applied Climatology · doi:10.1007/s007040070024 · Cited by 96 · see citing works

Application of neural networks and genetic algorithms for sizing of photovoltaic systems

Mellit, A., Kalogirou, S. A., & Drif, M. (2010) · Renewable Energy · doi:10.1016/j.renene.2010.04.017 · Cited by 90 · see citing works

2010 IEEE International Conference on Sustainable Energy Technologies (ICSET)

(2010) · doi:10.1109/icset17114.2010 · Cited by 57 · see citing works

Computational and Ambient Intelligence

Sandoval, F., Prieto, A., Graña, M., & Cabestany, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73007-1 · Cited by 50 · see citing works

Multi-ultrasonic sensor fusion for mobile robots

Yi, Z., Ho, Y.-K., Seng, C. C., & Wei, Z. (2002) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2000.898374 · Cited by 49 · see citing works

COORDINATION OF DISTRIBUTED ENERGY RESOURCE AGENTS

Li, J., Poulton, G., & James, G. C. (2010) · Applied Artificial Intelligence · doi:10.1080/08839514.2010.481483 · Cited by 41 · see citing works

Prediction of Solar Radiation Using Artificial Neural Networks

Faceira, J., Afonso, P., & Salgado, P. (2014) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-10380-8_38 · Cited by 28 · see citing works

Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

Li, J., Luo, S., & Jin, J. S. (2010) · Sensors · doi:10.3390/s101009384 · Cited by 18 · see citing works

A Novel 2-D Model Approach for the Prediction of Hourly Solar Radiation

Hocaoğlu, F. O., Gerek, Ö. N., & Kurban, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-73007-1_90 · Cited by 16 · see citing works

Application of Artificial Neural Network in Forecasting Solar Irradiance and Sizing of Photovoltaic Cell for Standalone Systems in Bangladesh

Quaiyum, S., Rahman, S., & Rahman, S. (2011) · International Journal of Computer Applications · Cited by 15 · see citing works

Renewable energy aggregation with intelligent battery controller

Li, J., Guo, Y., Platt, G., & Ward, J. (2013) · Renewable Energy · doi:10.1016/j.renene.2013.03.027 · Cited by 13 · see citing works

The Virtual Power Station

Platt, G., Guo, Y., Li, J., & West, S. (2008) · IEEE International Conference on Sustainable Energy Technologies · doi:10.1109/icset.2008.4747064 · Cited by 10 · see citing works

Application of support vector machine models for forecasting solar and wind energy resources: A review

Zendehboudi, A., Baseer, M. A., & Saidur, R. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.07.164 · Cited by 616 · see citing works

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Yang, D.-Z., Kleissl, J., Gueymard, C., Pedro, H., & Coimbra, C. (2018) · Solar Energy · doi:10.1016/j.solener.2017.11.023 · Cited by 508 · see citing works

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

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

Deep learning models for solar irradiance forecasting: A comprehensive review

Kumari, P., & Toshniwal, D. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128566 · Cited by 463 · see citing works

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

Benti, N. E., Chaka, M. D., & Semie, A. G. (2023) · Sustainability · doi:10.3390/su15097087 · Cited by 445 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

A review on the selected applications of forecasting models in renewable power systems

Ahmed, A., & Khalid, M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.09.046 · Cited by 326 · see citing works

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo, W. L., Lim, J. S., Ho, W. S., Hashim, H. B., & Lee, C. T. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.09.063 · Cited by 281 · see citing works

A current perspective on the accuracy of incoming solar energy forecasting

Blaga, R., Săbăduş, A., Stefu, N., Dughir, C., Paulescu, M., & Bădescu, V. (2018) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2018.10.003 · Cited by 269 · see citing works

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

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

Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network

Zang, H., Cheng, L., Ding, T., Cheung, K. W., Liang, Z. J., Wei, Z., & Sun, G. (2018) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2018.5847 · Cited by 246 · see citing works

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

Wang, F., Yu, Y., Zhang, Z., Li, J., Zhen, Z., & Li, K. (2018) · Applied Sciences · doi:10.3390/app8081286 · Cited by 192 · see citing works

Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Khandakar, A. A., Chowdhury, M. E. H., Kazi, M.-K., Benhmed, K., Touati, F., Al-Hitmi, M. A. E., & Gonzales, A. S. P. (2019) · Energies · doi:10.3390/en12142782 · Cited by 185 · see citing works

PV power forecasting based on data-driven models: a review

Gupta, P., & Singh, R. (2021) · International Journal of Sustainable Engineering · doi:10.1080/19397038.2021.1986590 · Cited by 168 · see citing works

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas, E., Allende‐Cid, H., & Rodrigo Salas, F. (2020) · Entropy · doi:10.3390/e22121412 · Cited by 158 · see citing works

Forecasting daily global solar irradiance generation using machine learning

Sharma, A., & Kakkar, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.066 · Cited by 146 · see citing works

Power output forecasting of solar photovoltaic plant using LSTM

Dhaked, D. K., Dadhich, S., & Birla, D. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100113 · Cited by 134 · see citing works

Integrated optimisation of photovoltaic and battery storage systems for UK commercial buildings

Mariaud, A., Acha, S., Ekins‐Daukes, N. J., Shah, N., & Markides, C. N. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.067 · Cited by 132 · see citing works

Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting

Kong, W., Jia, Y., Dong, Z. Y., Meng, K., & Chai, S. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115875 · Cited by 118 · see citing works

Solar radiation forecasting based on convolutional neural network and ensemble learning

Cannizzaro, D., Aliberti, A., Bottaccioli, L., Macii, E., Acquaviva, A., & Patti, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115167 · Cited by 113 · see citing works

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

Álvarez-Alvarado, J. M., Ríos-Moreno, J. G., Biosca, S. A. O., Ronquillo-Lomelí, G., Ventura-Ramos, E. J., & Trejo-Perea, M. (2021) · Applied Sciences · doi:10.3390/app11031044 · Cited by 112 · see citing works

Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

Saini, V. K., Kumar, R., Al‐Sumaiti, A. S., Sujil, A., & Heydarian‐Forushani, E. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109502 · Cited by 111 · see citing works

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Wojtkiewicz, J., Hosseini, M., Gottumukkala, R. N., & Chambers, T. L. (2019) · Energies · doi:10.3390/en12214055 · Cited by 108 · see citing works

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Agoua, G., Girard, R., & Kariniotakis, G. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2847558 · Cited by 104 · see citing works

A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights

Abisoye, B. O., Sun, Y., & Zenghui, W. (2023) · Renewable energy focus · doi:10.1016/j.ref.2023.100529 · Cited by 100 · see citing works

A Systematic Literature Review on big data for solar photovoltaic electricity generation forecasting

Viscondi, G. D. F., & Alves-Souza, S. N. (2018) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2018.11.008 · Cited by 96 · see citing works

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

Assaf, A. M., Haron, H., Hamed, H. N. A., Ghaleb, F. A. A., Qasem, S. N., & Albarrak, A. M. (2023) · Applied Sciences · doi:10.3390/app13148332 · Cited by 89 · see citing works

Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Dong, J., Olama, M. M., Kuruganti, T. P., Melin, A., Djouadi, S. M., Zhang, Y., & Xue, Y. S. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.05.073 · Cited by 86 · see citing works

The Impact of Digital Transformation on Environmental Sustainability

Truong, T. C. (2022) · Advances in Multimedia · doi:10.1155/2022/6324325 · Cited by 85 · see citing works

Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions

Tripathi, A. K., Aruna, M., Elumalai, P. V., Karthik, K., Khan, S. A., Asif, M., & Rao, K. S. (2024) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2024.104459 · Cited by 83 · see citing works

A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning

Jathar, L. D., Nikam, K. C., Awasarmol, U. V., Gurav, R. B., Patil, J. D., Shahapurkar, K., Soudagar, M. E. M., Khan, T. M. Y., Kalam, M. A., Hnydiuk-Stefan, A., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25407 · Cited by 81 · see citing works

Stratified Optimization Strategy Used for Restoration With Photovoltaic-Battery Energy Storage Systems as Black-Start Resources

Li, J., You, H., Qi, J., Kong, M., Zhang, S., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2937833 · Cited by 80 · see citing works

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

Nespoli, A., Niccolai, A., Ogliari, E., Perego, G., Collino, E., & Ronzio, D. A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117834 · Cited by 80 · see citing works

Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation

Abumohsen, M., Owda, A. Y., Owda, M., & Abumihsan, A. (2024) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2024.100636 · Cited by 78 · see citing works

Short-term renewable energy consumption and generation forecasting: A case study of Western Australia

Abu-Salih, B., Wongthongtham, P., Morrison, G. M., Coutinho, K., Al‐Okaily, M., & Huneiti, A. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e09152 · Cited by 76 · see citing works

Effective prediction model for Hungarian small‐scale solar power output

Abedinia, O., Raisz, D., & Amjady, N. (2017) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2017.0165 · Cited by 75 · see citing works

Review on Photovoltaic Power and Solar Resource Forecasting: Current Status and Trends

Carneiro, T. C., de Carvalho, P. C. M., dos Santos, H. A., Lima, M. A. F. B., & Braga, A. P. S. (2021) · Journal of Solar Energy Engineering · doi:10.1115/1.4051652 · Cited by 75 · see citing works

A taxonomical review on recent artificial intelligence applications to PV integration into power grids

Feng, C., Liu, Y., & Zhang, J. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107176 · Cited by 73 · see citing works

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

El‐kenawy, E. M., Mirjalili, S., Ghoneim, S. S. M., Eid, M. M., El-Said, M., Khan, Z. S., & Ibrahim, A. F. (2021) · IEEE Access · doi:10.1109/access.2021.3106233 · Cited by 72 · see citing works

Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network

Yadav, A. K., Sharma, V., Malik, H., & Chandel, S. S. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.06.023 · Cited by 72 · see citing works

Short-term photovoltaic power forecasting method based on irradiance correction and error forecasting

Ma, Y., Lv, Q., Zhang, R., Zhang, Y., Zhu, H., & Yin, W. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.167 · Cited by 65 · see citing works

An IoT environmental data collection system for fungal detection in crop fields

Truong, T., Dinh, A., & Wahid, K. A. (2017) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2017.7946787 · Cited by 63 · see citing works

Enhanced support vector regression based forecast engine to predict solar power output

Shang, C., & Wei, P. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.04.067 · Cited by 62 · see citing works

Solar Radiation Prediction Using Machine Learning Techniques: A Review

Paredes, E. D. O., Quintero, S. X. C., & Agudelo, J. P. (2019) · IEEE Latin America Transactions · doi:10.1109/tla.2019.8891934 · Cited by 60 · see citing works

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Jayalakshmi, N. Y., Shankar, R., Subramaniam, U., Baranilingesan, I., Karthick, A., Stalin, B., Rahim, R., & Ghosh, A. (2021) · Energies · doi:10.3390/en14092404 · Cited by 59 · see citing works

Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison

Sehrawat, N., Vashisht, S., & Singh, A. (2023) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2023.04.001 · Cited by 59 · see citing works

Multi-step solar irradiation prediction based on weather forecast and generative deep learning model

Gao, Y., Miyata, S., & Akashi, Y. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.02.051 · Cited by 58 · see citing works

Forecasting Hourly Solar Radiation Using Artificial Intelligence Techniques

Obiora, C. N., Hasan, A. N., Ali, A. A. Y., & Alajarmeh, N. S. (2021) · Canadian Journal of Electrical and Computer Engineering · doi:10.1109/icjece.2021.3093369 · Cited by 56 · see citing works

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Gao, M., Li, J., Hong, F., & Long, D. (2019) · Applied Sciences · doi:10.3390/app9153192 · Cited by 55 · see citing works

Hourly Solar Irradiance Forecasting Based on Machine Learning Models

Melzi, F. N., Touati, T., Samé, A., & Oukhellou, L. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0078 · Cited by 53 · see citing works

A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model

Kuo, P., & Huang, C. (2018) · Energies · doi:10.3390/en11040819 · Cited by 53 · see citing works

SVR-DEA model of carbon tax pricing for China's thermal power industry

An, Y., & Zhai, X. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139438 · Cited by 51 · see citing works

Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks

de Paiva, G. M., Pimentel, S. P., de Alvarenga, B. P., Marra, E. G., Mussetta, M., & Leva, S. (2020) · Energies · doi:10.3390/en13113005 · Cited by 48 · see citing works

Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models

Jamil, I., Hong, L., Iqbal, S., Aurangzaib, M., Jamil, R., Kotb, H., Alkuhayli, A. A., & AboRas, K. M. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.06.023 · Cited by 47 · see citing works

A Machine Learning-Based Model Predictive Control Method for Pumped Storage Systems

Cai, Q., Luo, X., Gao, C., Guo, P., Sun, S., Yan, S., & Zhao, P. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.757507 · Cited by 46 · see citing works

Forecast of hourly global horizontal irradiance based on structured Kernel Support Vector Machine: A case study of Tibet area in China

Jiang, H., & Dong, Y. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.03.054 · Cited by 46 · see citing works

Long term estimation of global horizontal irradiance using machine learning algorithms

Gupta, R., Yadav, A. K., Jha, S., & Pathak, P. K. (2023) · Optik · doi:10.1016/j.ijleo.2023.170873 · Cited by 42 · see citing works

A bibliometric analysis of machine learning techniques in photovoltaic cells and solar energy (2014–2022)

Zaidi, A. T. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.02.036 · Cited by 42 · see citing works

Illuminating the Future: A Comprehensive Review of AI-Based Solar Irradiance Prediction Models

Sammar, M. J., Saeed, M. A., Mohsin, S. M., Akber, S. M. A., Bukhsh, R., Abazeed, M. B., & Ali, M. (2024) · IEEE Access · doi:10.1109/access.2024.3402096 · Cited by 46 · see citing works

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Sharma, A., & Kakkar, A. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4366 · Cited by 41 · see citing works

AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning

Nadeem, A., Hanif, M. F., Naveed, M. S., Hassan, M. T., Gul, M., Husnain, N., & Mi, J. (2024) · AIMS Geosciences · doi:10.3934/geosci.2024035 · Cited by 42 · see citing works

Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method

Wang, J., Guo, L., Zhang, C., Song, L., Duan, J., & Duan, L. (2020) · Energy · doi:10.1016/j.energy.2020.118403 · Cited by 36 · see citing works

Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

Hosseini, M., Katragadda, S., Wojtkiewicz, J., Gottumukkala, R. N., Maida, A. S., & Chambers, T. L. (2020) · Energies · doi:10.3390/en13153914 · Cited by 36 · see citing works

Enhanced RBF neural network model for time series prediction of solar cells panel depending on climate conditions (temperature and irradiance)

Awad, M., & Qasrawi, I. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2779-5 · Cited by 34 · see citing works

A Multistage Hybrid Model ICEEMDAN-SE-VMD-RDPG for a Multivariate Solar Irradiance Forecasting

Sibtain, M., Li, X., Saleem, S., Qurat‐ul‐Ain, Asad, M. S., Tahir, T., & Apaydın, H. (2021) · IEEE Access · doi:10.1109/access.2021.3062764 · Cited by 33 · see citing works

A new solar power output prediction based on hybrid forecast engine and decomposition model

Zhang, W., Dang, H., & Simoes, R. (2018) · ISA Transactions · doi:10.1016/j.isatra.2018.06.004 · Cited by 31 · see citing works

Seasonal Clustering Forecasting Technique for Intelligent Hourly Solar Irradiance Systems

Omar, N. M., Aly, H. H., & Little, T. A. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3177746 · Cited by 28 · see citing works

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Nespoli, A., Mussetta, M., Ogliari, E., Leva, S., RAMIREZ, L. M. F., & García–Triviño, P. (2019) · Electronics · doi:10.3390/electronics8121434 · Cited by 28 · see citing works

Estimation of Hourly Global Solar Radiation Using Deep Learning Algorithms

Obiora, C. N., Ali, A. A. Y., & Hasan, A. N. (2020) · International Renewable Energy Congress · doi:10.1109/irec48820.2020.9310381 · Cited by 28 · see citing works

Big data and artificial intelligence application in energy field: a bibliometric analysis

Hou, Y., & Wang, Q. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-24880-y · Cited by 28 · see citing works

Forecasting Solar Energy: Leveraging Artificial Intelligence and Machine Learning for Sustainable Energy Solutions

Saadati, T., & Barutçu, B. (2025) · Journal of Economic Surveys · doi:10.1111/joes.12678 · Cited by 27 · see citing works

A deep learning model for multi-modal spatio-temporal irradiance forecast

Shan, S., Li, C., Wang, Y., Fang, S., Zhang, K., & Wei, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122925 · Cited by 26 · see citing works

Data-driven thermoelectric modeling: Current challenges and prospects

Mbaye, M. T., Pradhan, S. K., & Bahoura, M. J. (2021) · Journal of Applied Physics · doi:10.1063/5.0054532 · Cited by 27 · see citing works

A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

Mei, F., Pan, Y., Zhu, K., & Zheng, J. (2018) · Sustainability · doi:10.3390/su10030820 · Cited by 25 · see citing works

Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review

Gaamouche, R., Chinnici, M., Lahby, M., Abakarim, Y., & Hasnaoui, A. E. (2022) · Green energy and technology · doi:10.1007/978-3-030-96429-0_1 · Cited by 23 · see citing works

Wind Power Prediction Method: Support Vector Regression Optimized by Improved Jellyfish Search Algorithm

Yuan, D.-D., Li, M., Li, H.-Y., Lin, C., & Ji, B.-X. (2022) · Energies · doi:10.3390/en15176404 · Cited by 23 · see citing works

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Polimeni, S., Nespoli, A., Leva, S., Valenti, G., & Manzolini, G. (2021) · Processes · doi:10.3390/pr9020323 · Cited by 23 · see citing works

Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms

Obiora, C. N., Hasan, A. N., & Ali, A. A. Y. (2023) · Sustainability · doi:10.3390/su15118927 · Cited by 22 · see citing works

Photo-Voltaic (PV) Monitoring System, Performance Analysis and Power Prediction Models in Doha, Qatar

Touati, F., Khandakar, A. A., Chowdhury, M. E. H., Gonzales, A. S. P., Sorino, C. K., & Benhmed, K. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.92632 · Cited by 21 · see citing works

RTI-Net: Physics-informed deep learning for photovoltaic power forecasting

Li, H., Wu, W., Chen, W., & Zhang, M. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.124152 · Cited by 23 · see citing works

A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction

Gao, B., Yang, H., Lin, H., Wang, Z., Zhang, W., & Li, H. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2014187 · Cited by 20 · see citing works

Innovative approaches to solar energy forecasting: unveiling the power of hybrid models and machine learning algorithms for photovoltaic power optimization

Zhu, C., Wang, M., Guo, M., Deng, J., Du, Q., Wei, W., & Zhang, Y. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06504-z · Cited by 19 · see citing works

Prediction of solar irradiance with machine learning methods using satellite data

Ercan, U., & Koçer, A. (2024) · International Journal of Green Energy · doi:10.1080/15435075.2024.2305857 · Cited by 19 · see citing works

Sensitivity‐based and optimization‐based methods for mitigating voltage fluctuation and rise in the presence of PV and PHEVs

Ali, A., Raisz, D., & Mahmoud, K. (2017) · International Transactions on Electrical Energy Systems · doi:10.1002/etep.2456 · Cited by 18 · see citing works

Solar panel energy production forecasting by machine learning methods and contribution of lifespan to sustainability

Yılmaz, H., & Şahin, M. (2023) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-023-05110-5 · Cited by 17 · see citing works

Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms

Shukla, A., Pachauri, R. K., Hussain, A., Ali, A. A. Y., & Khan, B. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105024 · Cited by 17 · see citing works

Performance Ratio Estimation and Prediction of Solar Power Plants Using Machine Learning to Improve Energy Reliability

Bandong, S., Leksono, E., Purwarianti, A., & Joelianto, E. (2019) · International Conference on Instrumentation, Control, and Automation (ICA) · doi:10.1109/ica.2019.8916687 · Cited by 17 · see citing works

Method of monitoring and detection of failures in PV system based on machine learning

Benavides, D., Arévalo-Cordero, P., González, L. G., Hernández-Callejo, L., Jurado, F., & Aguado, J. A. (2020) · Revista Facultad de Ingeniería Universidad de Antioquia · doi:10.17533/udea.redin.20200694 · Cited by 17 · see citing works

Time Series Prediction of Solar Power Generation Using Trend Decomposition

Kavakci, G., Cicekdag, B., & Ertekin, Ş. (2023) · Energy Technology · doi:10.1002/ente.202300914 · Cited by 16 · see citing works

Solar/wind pumping system with forecasting in Sharjah, United Arab Emirates

Obaid, W., Hamid, A.-K., & Ghenaï, C. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i4.pp2752-2759 · Cited by 16 · see citing works

The forecasting of solar energy based on Machine Learning

Jebli, I., Belouadha, F.-Z., & Kabbaj, M. I. (2020) · International Conference on Electrical and Information Technologies · doi:10.1109/iceit48248.2020.9113168 · Cited by 15 · see citing works

A Comparative Analysis of Artificial Neural Networks for Photovoltaic Power Forecast Using Remotes and Local Measurements

Lopes, S. M. A., Cari, E. P. T., & Hajimirza, S. (2021) · Journal of Solar Energy Engineering · doi:10.1115/1.4053031 · Cited by 15 · see citing works

Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data

Gerges, F., Boufadel, M. C., Bou-Zeid, E. R., Nassif, H. H., & Wang, J. T. L. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01955-x · Cited by 14 · see citing works

Estimation and analysis of missing temperature data in high altitude and snow-dominated regions using various machine learning methods

Gezici, K., & Şengül, S. (2023) · Environmental Monitoring and Assessment · doi:10.1007/s10661-023-11143-7 · Cited by 13 · see citing works

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Yadav, O., Kannan, R., Meraj, S. T., & Masaoud, A. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/5680635 · Cited by 13 · see citing works

Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

Tolba, H., Dkhili, N., Nou, J., Eynard, J., Thil, S., & Grieu, S. (2020) · Energies · doi:10.3390/en13164184 · Cited by 13 · see citing works

Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification

Xu, F., Tang, R. X., Xu, S. B., Fan, Y. L., Zhou, Y., & Zhang, H. T. (2021) · Energy · doi:10.1016/j.energy.2020.119748 · Cited by 13 · see citing works

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis, A., Mylonas, C., Plakas, K. A., Birbas, M., & Birbas, A. N. (2023) · IEEE Access · doi:10.1109/access.2023.3335191 · Cited by 12 · see citing works

Modeling the economic impact of incoming tropical cyclones using machine learning

Wendler-Bosco, V., & Nicholson, C. (2021) · Natural Hazards · doi:10.1007/s11069-021-04955-8 · Cited by 12 · see citing works

Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines

Fuselero, A. D. P., Portus, H. M. S. A., & Doma, B. T. (2022) · International Journal of Renewable Energy Development · doi:10.14710/ijred.2022.44755 · Cited by 12 · see citing works

Combined Machine Learning and Physics-Based Forecaster for Intra-day and 1-Week Ahead Solar Irradiance Forecasting Under Variable Weather ConditionsPreprint

Riggs, H., Tufail, S., Tariq, M., & Sarwat, A. I. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.09073

Forecasting of Solar Irradiance using Machine Learning Techniques

Mishra, S., Pandey, D., & Bhardwaj, S. (2024) · International Conference on Signal Processing and Advance Research in Computing (SPARC) · doi:10.1109/sparc61891.2024.10828799 · Cited by 1 · see citing works

Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power

Rodríguez, F., Martín, F., Fontán, L., & Galarza, A. (2021) · Energy · doi:10.1016/j.energy.2021.120647 · Cited by 57 · see citing works

Forecasting Short-Term Solar Irradiance and Power Generation of Solar Pv Power Station Through Machine Learning ModelsPreprint

Jaini, S. N. B., & Lee, D. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3912601

Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

Viscondi, G. D. F., & Alves-Souza, S. N. (2021) · Energies · doi:10.3390/en14185657 · Cited by 44 · see citing works