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Artificial Intelligence and the Global Transition to Renewable Energy: Trends, Perceptions, and Policy Pathways

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 4815

Special Issue Editors

Dr. András Szeberényi

E-Mail Website
Guest Editor
Institute of Marketing, Budapest Metropolitan University, Nagy Lajos Kiraly utja,1–9, 1148 Budapest, Hungary
Interests: marketing and media; online communication; search engine optimization (SEO); renewable energy; environmental awareness and protection; environmental sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Norbert Bozsik

E-Mail Website
Guest Editor
Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, 2100 Gödöllő, Hungary
Interests: sustainable development; renewable energy; energy security; land use; competitiveness; agricultural economics; agricultural development; international agricultural trade; food security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global shift toward renewable energy sources, such as solar, wind, biomass, and hydropower, is central to achieving climate neutrality, energy security, and sustainable development. However, this transition is not solely a technological challenge but also a deeply systemic transformation involving economic, environmental, and societal dimensions. Amid these complexities, artificial intelligence (AI) emerges as a powerful enabler of innovation, optimization, and policy integration within the renewable energy sector.

This Special Issue will explore the international status, public perception, and implementation dynamics of renewable energy, with a particular emphasis on the applications and implications of AI in accelerating this transformation. We invite scholarly contributions that examine how AI technologies, ranging from machine learning and predictive analytics to autonomous control systems, can support the integration of renewables into national grids, optimize energy forecasting, manage decentralized systems, and inform strategic policymaking.

In addition, we welcome interdisciplinary studies addressing societal attitudes toward renewable energy adoption, regional disparities in development, and the ethical and governance considerations of AI deployment in the energy sector. By combining technological insight with socio-political analysis, this Special Issue will offer a comprehensive perspective on how intelligent systems can contribute to a more resilient, efficient, and publicly accepted renewable energy future.

This Special Issue welcomes contributions related (but not limited) to the following topics:

  • Applications of artificial intelligence in the integration and management of renewable energy systems;
  • Public attitudes and international comparisons of renewable energy acceptance;
  • AI-driven forecasting and optimization in solar, wind, and bioenergy production;
  • Ethical, legal, and policy implications of AI in renewable energy governance;
  • Comparative analysis of national strategies and policy frameworks supporting renewable energy transitions.

Dr. András Szeberényi
Dr. Norbert Bozsik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • renewable energy transition
  • artificial intelligence in energy
  • public perception
  • energy policy and governance
  • sustainable technology adoption

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Published Papers (4 papers)

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Research

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22 pages, 1653 KB  
Article
Public Acceptance of Renewable Energy in a Post-Socialist, Energy Import-Dependent Context: Evidence from Hungary
by Ágnes Fűrész, Norbert Bozsik and András Szeberényi
Energies 2026, 19(4), 931; https://doi.org/10.3390/en19040931 - 11 Feb 2026
Viewed by 570
Abstract
Public acceptance is a key prerequisite for renewable energy deployment, yet evidence from post-socialist, energy import-dependent countries remains limited, and acceptance is often treated as a single construct. This study examines Hungary and distinguishes between (i) general societal support for renewable energy and [...] Read more.
Public acceptance is a key prerequisite for renewable energy deployment, yet evidence from post-socialist, energy import-dependent countries remains limited, and acceptance is often treated as a single construct. This study examines Hungary and distinguishes between (i) general societal support for renewable energy and (ii) individual-level commitment to adoption. Using an online survey conducted in October–November 2024 (N = 417), we test for an acceptance gap and assess attitudinal drivers with paired-sample t-tests, OLS regression, and cluster-based comparisons. Results show a significant acceptance gap: general societal support exceeds individual-level commitment (mean difference = 0.17 on a three-point scale; Cohen’s d = 0.36; p < 0.001). In bivariate terms, perceived economic benefits exhibit only a weak association with acceptance, but in multivariate models they emerge as a strong predictor of individual-level commitment (β = 0.600; R2 = 0.407), whereas environmental attitudes and energy security perceptions show weaker and non-significant independent effects. Cluster analysis further indicates heterogeneous attitudinal profiles and varying levels of acceptance, suggesting that economic evaluations operate as an enabling dimension within broader attitudinal configurations rather than a standalone driver. These findings highlight why broad societal endorsement may not translate into personal engagement and imply that policy strategies should complement general pro-renewable narratives with measures that address perceived feasibility and individual-level costs and uncertainties. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Global Transition to Renewable Energy: Trends, Perceptions, and Policy Pathways)
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Review

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15 pages, 457 KB  
Review
AI-Driven Adaptive Urban Lighting for Reducing Light Pollution and Energy Consumption in a Multi-Level Perspective
by Dalma Bódizs, Anikó Zseni and Dalma Schmeller
Energies 2026, 19(5), 1128; https://doi.org/10.3390/en19051128 - 24 Feb 2026
Viewed by 793
Abstract
Urban lighting systems contribute significantly to energy consumption and light pollution, raising environmental and societal concerns. This paper explores the potential of Artificial Intelligence (abbreviation: AI)-driven adaptive urban lighting as a sustainable solution, framed within a multi-level perspective on socio-technical transitions. At the [...] Read more.
Urban lighting systems contribute significantly to energy consumption and light pollution, raising environmental and societal concerns. This paper explores the potential of Artificial Intelligence (abbreviation: AI)-driven adaptive urban lighting as a sustainable solution, framed within a multi-level perspective on socio-technical transitions. At the landscape level, increasing urbanization and global sustainability targets exert pressure for energy-efficient practices, while traditional street lighting regimes remain largely rigid and resource-intensive. At the niche level, we propose a novel adaptive lighting system integrating real-time Internet of Things (abbreviation: IoT) sensor data and machine learning algorithms to dynamically adjust illumination based on traffic, pedestrian activity, weather conditions, and ambient light. Studies demonstrate that the proposed approach can significantly reduce energy use while minimizing light pollution, without compromising safety or visibility. The results indicate that such niche innovations, supported by AI and renewable energy integration, have the potential to influence broader regime change and contribute to sustainable urban development. This research highlights the importance of combining technological innovation with socio-technical frameworks to address pressing urban environmental challenges, offering insights for policymakers, urban planners, and energy managers seeking to balance efficiency, safety, and ecological impact. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Global Transition to Renewable Energy: Trends, Perceptions, and Policy Pathways)
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32 pages, 1035 KB  
Review
Charting Smarter Skies—A Review of Computational Strategies for Energy-Saving Flights in Electric UAVs
by Graheeth Hazare, Mohamed Thariq Hameed Sultan, Andrzej Łukaszewicz, Marek Nowakowski and Farah Syazwani Shahar
Energies 2025, 18(24), 6521; https://doi.org/10.3390/en18246521 - 12 Dec 2025
Viewed by 909
Abstract
This review surveys the past five years of research on energy-aware path optimization for both solar-powered and battery-only UAVs. First, the energy constraints of these two platforms are contrasted. Next, advanced computational frameworks—including model predictive control, deep reinforcement learning, and bio-inspired metaheuristics—are examined [...] Read more.
This review surveys the past five years of research on energy-aware path optimization for both solar-powered and battery-only UAVs. First, the energy constraints of these two platforms are contrasted. Next, advanced computational frameworks—including model predictive control, deep reinforcement learning, and bio-inspired metaheuristics—are examined along with their hardware implementations. Recent studies show that hybrid methods combining neural networks with bio-inspired search can boost net energy efficiency by 15–25% while maintaining real-time feasibility on embedded GPUs or FPGAs. Among the remaining challenges are federated learning at the edge, multi-UAV coordination under partial observability, and field trials on ultra-long-endurance platforms like the Airbus Zephyr HAPS. Addressing these issues will accelerate the deployment of truly persistent and green aerial services. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Global Transition to Renewable Energy: Trends, Perceptions, and Policy Pathways)
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Other

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71 pages, 3197 KB  
Systematic Review
Applications of Artificial Intelligence in Renewable Energy Transition: A Systematic Literature Review
by Shahbaz Ahmad Saadi, Dhanashree Katekhaye and Róbert Magda
Energies 2026, 19(8), 1839; https://doi.org/10.3390/en19081839 - 9 Apr 2026
Viewed by 1530
Abstract
The renewable energy transition is a central component of global strategies to mitigate climate change and achieve sustainable development. However, the large-scale integration of renewable energy sources introduces significant challenges related to variability, system complexity, and operational efficiency. In recent years, artificial intelligence [...] Read more.
The renewable energy transition is a central component of global strategies to mitigate climate change and achieve sustainable development. However, the large-scale integration of renewable energy sources introduces significant challenges related to variability, system complexity, and operational efficiency. In recent years, artificial intelligence (AI) has emerged as a promising enabler for addressing these challenges through advanced data-driven forecasting, optimization, and decision-support capabilities. This study presents a systematic bibliometric and thematic review of peer-reviewed research on AI applications in the renewable energy transition published between 2015 and 2025, and was conducted following the PRISMA framework. Using the Scopus database, a total of 595 journal articles were analyzed through bibliometric performance indicators, network analysis, and thematic synthesis. The results reveal a rapidly growing and highly collaborative research field, characterized by strong international co-authorship and increasing methodological diversity. Early research predominantly focused on prediction and forecasting tasks, while more recent studies emphasize system-level optimization, energy management, and integrative AI applications across renewable technologies. The review further highlights key research trends, conceptual framing, and methodological orientations shaping the field. By consolidating dispersed literature and mapping its evolution, this study provides a structured overview that supports future research, policy development, and practical implementation of AI-enabled solutions for a sustainable energy transition. Full article
(This article belongs to the Special Issue Artificial Intelligence and the Global Transition to Renewable Energy: Trends, Perceptions, and Policy Pathways)
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