Artificial Intelligence for Smart Renewable Energy Sector in Europe—Smart Energy Infrastructures for Next Generation Smart Cities
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One of the most challenging areas of Future Smart Cities Research is the Smart Energy domain. Critical issues related to optimization, provision of smart customizable networks and sophisticated computational techniques and methods enabled by artificial intelligence and machine learning need further investigation. The renewable energy (RE) is a powerful resource for the future global development in the context of climate change and resources depletion. Artificial intelligence (AI) implies new rules of organizing the activities in order to respond to these new requirements. It is necessary to improve the design of the energy infrastructure, the deployment and production of RE in order to face the multiple challenges that will affect the sector’s growth and resilience.. In this research work we exploit the recent developments on the AI adoption for RE sector in European Union (EU). In this respect, we analysed (i) the efficiency of the transformation processes of the RE within the energy chain from Gross Inland Consumption to Final Energy Consumption, (ii) its implications on the structure of renewable energy by source (solar, wind, biomass etc.), (iii) the labour productivity in RE sector compared to the economy as a whole and its correlation with investments level, (iv) the implication of the adoption of AI for RE towards Future Smart Cities Research. The main contribution of this research is the development of a framework for understanding the contribution of AI in the RE sector in Europe. Another bold contribution of this work is the discussion of the implications for Future Smart Cities Research and future research directions.
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Environmentally smart sustainable urbanism involves developing joint actions for preserving the environment based on analyzing large-scale databases, understanding climate change, modeling its impacts, improving ecosystem health, enabling high integration of renewable energy and smart energy, enhancing smart renewable energy infrastructures, optimizing energy consumption and production, developing
“This lies in developing joint actions for preserving the environment based on analyzing large-scale databases, understanding the complexity of climate change and modeling and simulating its potential impacts, improving the health of ecosystems, and enabling high integration of renewable energy and smart energy [271], enhancing smart renewable energy infrastructures in smart cities [275],”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [275]
ANN, Deep Neural Network (DNN), SVM, GA, LR, and DT are among the prominent AI techniques that gained strong traction in recent years, as are ML, DL, and cognitive AI models in relation to the planning and development of smart cities.
“As consistent with the findings for the second and third periods, ANN, Deep Neural Network (DNN), SVM, GA, LR, and DT are among the prominent AI techniques that gained strong traction in recent years (Şerban and Lytras 2020; Shrestha and Mahmood 2019), so are ML, DL, and cognitive AI models in relation to the planning and development of smart cities (Kamrowska-Załuska 2021; Şerban and Lytras 2020”
From Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review (2023) · cites this paper as Şerban and Lytras 2020
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References 47 · Cited by 300
Research works printed but not linked: 7
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- The Role Of Science, Technology And Innovation In Promoting Renewable Energy By 2030, United Nations Conference On Trade And Development, United Nations, Geneva, Switzerland, 2019.
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- A. Visvizi and M. D. Lytras, Smart Cities: Issues and Challenges: Map- ping Political, Social and Economic Risks and Threats. Amsterdam, The Netherlands: Elsevier, 2019.
- Authors
- Serban, A. C., & Lytras, M. D.
- Affiliations
- Department of Economics and Economic Policy, Bucharest University of Economic Studies, Bucharest, Romania
- Effat University (SA)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 77364-77377
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3017872050
- Cited by
- 300
- References
- 47
- Citations by year
- 2026: 45 · 2025: 74 · 2024: 74 · 2023: 42 · 2022: 39 · 2021: 23 · 2020: 3
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Economy, and Technology Trends
- Keywords
- artificial intelligence, renewable energy, smart cities, energy transition, energy efficiency, labor productivity, renewable energy investment, European Union
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