Applications of Artificial Intelligence in Renewable Energy Transition: A Systematic Literature Review

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 (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.

1. Introduction

The global energy sector is under increasing pressure from climate change, rising demand, and the environmental costs of fossil-fuel dependence. As a result, many countries are accelerating the transition toward renewable energy sources such as solar, wind, hydro, and biomass [1]. Although renewables offer a cleaner alternative, their large-scale integration remains difficult due to intermittency, forecasting uncertainty, and grid stability challenges. Managing these complexities has therefore become a central concern for modern energy systems.

In this context, artificial intelligence (AI) has emerged as a promising enabler of the renewable energy transition. AI techniques, including machine learning and deep learning, can process large volumes of data, identify patterns, and support more informed decision-making. In practice, AI-based models have been widely applied to improve solar and wind forecasting, reduce operational uncertainty, and strengthen grid reliability.

Recent studies indicate that AI contributes to smarter and more efficient renewable energy systems by supporting real-time energy management, demand response, predictive maintenance, and storage optimization [2]. Cross-country evidence further suggests that AI integration can enhance innovation capacity and sustainability outcomes, reinforcing its strategic relevance for clean energy goals [3]. Despite this rapid growth, however, the literature remains fragmented. Much of the existing research is technology-centric, focusing on isolated applications such as forecasting models or grid optimization methods rather than examining the broader system-level dynamics of renewable energy transitions. Broader economic, regulatory, and institutional implications are also frequently underexplored, particularly in developing economies where digital infrastructure, skilled human capital, and policy readiness can limit effective AI adoption [4].

In reality, renewable energy deployment increasingly occurs within integrated energy systems, where electricity generation, storage technologies, demand-side management, and grid operations interact simultaneously. Studies on integrated energy systems highlight the importance of addressing uncertainty, reliability, and supply–demand coordination when managing renewable energy resources. These system-level challenges suggest that AI applications should not be viewed solely as tools for optimizing individual technologies, but rather as mechanisms that support coordination across interconnected components of modern energy infrastructures.

Artificial intelligence is increasingly recognized as a general-purpose technology capable of enabling innovation across sectors by improving efficiency, reducing uncertainty, and optimizing complex processes at scale [5,6]. Within energy systems, its role extends beyond technical forecasting toward integrated system planning, energy management, and strategic decision support across interconnected infrastructures. At the same time, the growing use of AI raises important concerns related to transparency, ethical governance, cybersecurity risks, and unequal access to digital infrastructure. These considerations highlight the importance of examining AI not only from a technological perspective but also within the broader socio-technical context of energy transitions.

Despite the expanding body of research in this field, existing studies remain dispersed across different technologies, analytical methods, and geographic contexts. As a result, there is still limited understanding of how AI applications collectively contribute to the renewable energy transition at a broader system level. To address this fragmentation, the present study organizes the literature through a domain-wise classification of AI applications supported by bibliometric mapping techniques, allowing the identification of key research clusters, thematic relationships, and emerging trends across the field.

By combining systematic literature review procedures with bibliometric analysis, this study provides a structured synthesis of research on AI-enabled renewable energy systems. In contrast to reviews focusing on individual technologies, the present study adopts a system-level perspective, examining AI applications across multiple functional domains including forecasting, optimization and control, predictive maintenance, and strategic decision support. This approach enables a more comprehensive understanding of how AI contributes to the renewable energy transition as an interconnected socio-technical system.

Research Aim and Objectives

This study aims to synthesize existing knowledge on the applications of artificial intelligence in advancing the renewable energy transition, with particular attention to how AI supports efficiency, reliability, and sustainability in renewable energy systems.

Accordingly, the study is guided by the following objectives:

• To systematically examine the existing body of literature on the application of artificial intelligence techniques within renewable energy systems, including solar energy, wind energy, energy storage technologies, and smart grid infrastructures.
• To analyze how AI-based approaches are applied in renewable energy systems through bibliometric mapping and thematic classification of the literature.
• To identify key research challenges and constraints discussed in the literature regarding the adoption of AI in renewable energy systems, including issues related to data accessibility, cybersecurity risks, regulatory frameworks, and infrastructural readiness.
• To identify emerging AI methodologies and outline potential future research directions that may support the development of scalable, resilient, and sustainable renewable energy systems.

2. Literature Review

2.1. Artificial Intelligence and Renewable Energy Transition

The global transition toward renewable energy systems is driven by the need to mitigate climate change, reduce dependence on fossil fuels, and ensure long-term energy security. However, the large-scale integration of renewable energy sources such as solar and wind introduces challenges related to intermittency, uncertainty, system complexity, and grid stability. Within this context, Artificial Intelligence (AI) has emerged as a key technological enabler, providing advanced analytical and decision-support capabilities that complement conventional energy management approaches [7,8,9,10].

Renewable energy systems operate as complex socio-technical infrastructures in which generation, storage, distribution, and consumption are dynamically interconnected. AI techniques, particularly machine learning and data-driven optimization, support adaptive learning, predictive control, and real-time system monitoring across these interconnected layers [9,11]. These capabilities enable energy systems to respond more effectively to variability in renewable generation and demand patterns.

AI also contributes to sustainable energy management by improving forecasting accuracy, operational planning, and risk management. Unlike traditional deterministic energy models, AI-based approaches leverage large datasets and probabilistic learning to manage the stochastic behavior of renewable energy sources [12]. These features make AI particularly valuable in addressing the operational uncertainty associated with renewable energy integration.

Furthermore, AI technologies facilitate the transformation of traditional centralized energy systems toward decentralized and intelligent energy networks. Smart grids and digital energy platforms increasingly incorporate AI-based algorithms to support demand-side management, real-time optimization, and distributed energy resource coordination [13,14]. Through these capabilities, AI supports both technical optimization and long-term strategic planning in renewable energy systems.

2.2. Existing Reviews and Research Trends

Recent studies show a rapid expansion of research examining the intersection of artificial intelligence and renewable energy systems. Early review studies primarily focused on the application of machine learning techniques for renewable energy forecasting and system optimization. However, more recent reviews indicate a broader scope of AI applications, including energy management, predictive maintenance, and grid resilience.

Systematic and bibliometric reviews highlight that AI applications have gradually evolved from isolated forecasting tasks toward integrated energy management solutions. Studies focusing on smart energy systems emphasize AI-driven approaches for demand prediction, fault detection, adaptive control, and system reliability in environments with high renewable penetration [15].

The literature also reveals increasing interest in explainable artificial intelligence (XAI) and transparent decision-making models. These developments reflect growing concerns regarding interpretability and governance in critical infrastructure systems [13,16]. At the same time, bibliometric mapping studies demonstrate a significant rise in AI-related renewable energy research after 2017, driven by advances in deep learning and global decarbonization policies [17].

Overall, the existing literature indicates a clear transition from isolated AI applications toward more integrated and system-oriented energy solutions. However, many studies remain technology-focused and fragmented across domains, highlighting the need for integrative reviews that map research themes and identify emerging directions in AI-enabled renewable energy systems [18,19].

2.3. Classification of Artificial Intelligence Applications in the Renewable Energy Transition

The literature suggests that AI applications in renewable energy systems can be broadly classified based on their operational roles. Four major categories frequently appear in previous studies: forecasting and prediction, optimization and control, predictive maintenance, and decision-support applications [9,14].

2.3.1. Forecasting and Prediction

Forecasting represents one of the most widely studied AI applications in renewable energy systems. Accurate prediction of solar irradiance, wind speed, and electricity demand is essential for managing the variability of renewable energy sources. Machine learning and deep learning models, such as artificial neural networks, support vector machines, and recurrent neural networks, are widely used to enhance short- and long-term forecasting accuracy [20,21].

2.3.2. Optimization and Control

AI techniques are also widely applied for optimization and control in renewable energy systems. Reinforcement learning and evolutionary algorithms support real-time decision-making in smart grids and hybrid energy systems, improving energy dispatch, storage management, and power flow control [22].

2.3.3. Predictive Maintenance and System Reliability

Predictive maintenance has become an important application area for AI in renewable energy infrastructure. AI-based diagnostic models analyze sensor data and historical operational records to detect anomalies and predict equipment failures in systems such as wind turbines and photovoltaic installations [23].

2.3.4. Decision Support and Strategic Planning

Beyond operational applications, AI is increasingly used to support strategic energy planning and policy evaluation. AI-driven models assist in scenario analysis, investment planning, and grid expansion strategies, helping policymakers and energy planners evaluate trade-offs between economic performance, environmental sustainability, and system resilience [24].

2.3.5. Emerging and Hybrid AI Application Categories

Recent literature also identifies emerging classifications that combine multiple AI techniques and application domains. Hybrid models integrating forecasting, optimization, and explainable AI are gaining attention for their potential to improve transparency and trust in AI-driven energy systems. Additionally, the integration of AI with digital twins, Internet of Things (IoT) platforms, and cyber-physical energy systems reflects a shift toward more holistic and intelligent renewable energy ecosystems [1]. These developments indicate a progression from isolated AI applications toward system-wide intelligence in renewable energy transition efforts.

2.4. AI Techniques Overview

A variety of AI techniques are applied in renewable energy research. Machine learning algorithms, including artificial neural networks, support vector machines, and decision trees, are widely used for forecasting, classification, and performance evaluation tasks [21].

Deep learning approaches, such as convolutional neural networks and recurrent neural networks, have become increasingly popular for handling large-scale renewable energy datasets and capturing temporal patterns in energy production and demand [25]. Reinforcement learning methods are commonly applied in optimization and control tasks, particularly for energy dispatch and demand response management in smart grids [9,26].

Recent research also explores hybrid and advanced AI approaches that combine multiple learning paradigms to improve model performance and transparency. Hybrid ML–DL models and explainable AI frameworks aim to enhance both predictive accuracy and interpretability, reflecting a growing emphasis on trustworthy AI systems in energy infrastructure [1].

2.5. Bibliometric Research and Knowledge Mapping

Bibliometric analysis has become an important methodological approach for examining the intellectual structure and evolution of scientific research fields [27,28]. By analyzing publication outputs, citation relationships, and keyword networks, bibliometric techniques enable researchers to identify influential studies, collaboration patterns, and emerging research themes [29,30]. Unlike traditional narrative reviews, bibliometric methods provide a quantitative perspective on how knowledge develops and diffuses across disciplines and geographical regions [31].

Recent studies increasingly combine bibliometric analysis with systematic literature review procedures in order to provide both quantitative mapping and qualitative interpretation of research trends [32,33]. Techniques such as co-citation analysis, bibliographic coupling, and keyword co-occurrence analysis allow scholars to uncover thematic clusters and evolving research fronts within a given domain [34]. In rapidly expanding fields such as artificial intelligence applications in renewable energy systems, bibliometric mapping is particularly valuable for identifying dominant research topics, emerging methodological approaches, and potential gaps in the literature [35].

3. Materials and Methods

This study adopts a systematic literature review (SLR) combined with bibliometric analysis to examine how artificial intelligence (AI) has been applied in the context of the renewable energy transition (RET) and how this body of research has evolved. The methodological protocol was developed to ensure transparency, consistency, and reproducibility, following established practices for systematic literature reviews and bibliometric studies. By integrating structured screening procedures with quantitative bibliometric techniques, the study provides a reliable overview of a rapidly expanding and interdisciplinary research field [36]. Bibliometric approaches are particularly useful for mapping large research domains; however, the interpretation of bibliometric indicators requires careful consideration to avoid overemphasizing citation counts or overlooking emerging research themes.

3.1. Contextual Background: Artificial Intelligence and the Renewable Energy Transition

The global energy sector is experiencing a profound transformation driven by the urgency of climate change mitigation, growing energy security concerns, and the pursuit of long-term sustainability. International policy initiatives and agreements, most notably net-zero emission targets, have accelerated the deployment of renewable energy technologies such as solar, wind, hydro, and hybrid systems [37]. While these technologies offer clear environmental benefits, their large-scale integration into existing energy systems presents persistent challenges, including the intermittent nature of renewable generation, uncertainty in energy forecasting, grid stability constraints, and the increasing complexity of managing distributed and decentralized energy resources.

Against this backdrop, artificial intelligence has gained prominence as a technological enabler capable of addressing several of these systemic challenges. AI-based approaches, including machine learning, deep learning, reinforcement learning, and advanced optimization techniques—have been widely employed to improve forecasting accuracy, enable real-time operational control, support predictive maintenance, and enhance decision-making across renewable energy systems [38,39]. At the same time, the growing availability of high-resolution energy data, together with advances in computational power, has facilitated the practical application of AI methods beyond experimental settings.

The convergence of AI and the renewable energy transition reflects a broader shift toward intelligent, adaptive, and increasingly autonomous energy systems. In addition to technical optimization, AI-driven solutions contribute to system resilience, operational efficiency, and strategic planning for sustainable energy infrastructures [40]. Given the rapid expansion, methodological diversity, and interdisciplinary nature of this research area, a systematic literature review combined with bibliometric analysis is essential to synthesize existing knowledge, identify dominant themes, and reveal gaps that remain insufficiently addressed in the literature.

3.2. Research Design and Systematic Review Framework

A structured and sequential research design was employed to examine the literature on AI applications in the renewable energy transition through bibliometric and thematic analysis [36]. The overall research process is guided by a systematic review framework that integrates data collection, quantitative bibliometric analysis, network visualization, and qualitative synthesis, thereby ensuring methodological rigor and analytical coherence [41,42].

This systematic literature review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines to ensure transparency and reproducibility in the review process [43,44].

Figure 1 illustrates the research framework adopted in this study. The framework outlines the complete review process, beginning with the identification of relevant publications through a structured database search. This is followed by screening and eligibility assessment, leading to the construction of the final bibliometric dataset. Subsequent stages involve bibliometric and network analyses to examine publication trends, collaboration structures, and thematic patterns within the literature.

The framework extends beyond descriptive analysis by incorporating a results and discussion phase, in which bibliometric findings are interpreted within the broader discourse on AI-enabled renewable energy systems. Building on these insights, the final stage of the framework focuses on the formulation of future research propositions, highlighting emerging research directions and opportunities for advancing AI-driven solutions in the renewable energy transition.

3.3. Search Strategy and Database

The Scopus database was selected as the primary source of bibliographic data due to its broad and multidisciplinary coverage of peer-reviewed journals in energy systems, engineering, and computer science. A structured search strategy was developed to capture studies addressing the application of artificial intelligence within renewable energy systems and the broader energy transition context.

The search query was applied to article titles, abstracts, and author keywords using the following expression:

TITLE-ABS-KEY (“renewable energy systems”) AND

(“artificial intelligence” OR “AI” OR “machine learning” OR “deep learning”)

The search query was intentionally restricted to the phrase “renewable energy systems” to focus on studies addressing integrated renewable energy infrastructures rather than isolated technological applications. While broader terms such as “solar energy”, “wind energy”, “smart grid”, or “energy transition” may capture a wider body of literature, this restriction was applied to ensure conceptual consistency and relevance to system-level analysis. This approach prioritizes thematic coherence over dataset breadth and aligns with the objective of examining AI applications within integrated renewable energy systems. However, this choice may limit coverage of some domain-specific studies, which is acknowledged as a limitation of the review.

To reflect contemporary developments in AI techniques and renewable energy research, the search was limited to publications between 2015 and 2025. The study selection process is illustrated in a PRISMA flow diagram (Figure 2), which outlines the identification, screening, eligibility, and inclusion stages.

3.4. Inclusion and Exclusion Criteria

To ensure the relevance and quality of the dataset, predefined inclusion and exclusion criteria were applied. The review included only peer-reviewed journal articles published in English that explicitly addressed AI-based methods in renewable energy systems or the renewable energy transition. Publications outside the selected time frame or focusing solely on conventional energy systems were excluded. In addition, conference papers, book chapters, editorials, and notes were removed from the dataset.

Inclusion decisions were based on three key criteria: (i) explicit application of artificial intelligence techniques, (ii) relevance to renewable energy systems or transition contexts, and (iii) a clear methodological or empirical contribution. Studies not meeting at least two of these criteria were excluded to ensure thematic relevance and analytical consistency.

3.5. Screening and Data Extraction Process

The initial database search yielded 1128 records. After the removal of duplicate entries, titles and abstracts were screened for relevance based on the predefined inclusion criteria. Relevance at this stage was assessed by evaluating whether the study explicitly addressed AI applications within renewable energy systems or transition-related contexts. Studies with unclear scope or lacking a direct connection to AI or renewable energy applications were excluded.

Following this screening process, 902 publications remained. A further filtering step restricted the dataset to peer-reviewed journal articles only, resulting in a final sample of 595 publications for detailed analysis. Appendix A presents all the publications included in the systematic review.

For each selected article, bibliographic metadata, including authorship information, publication year, journal title, country affiliations, author keywords, abstracts, and citation data, were extracted directly from the Scopus database. The screening and extraction procedures were carried out systematically to minimize selection bias and ensure consistency across the dataset.

Country contributions were calculated using a full counting approach based on author affiliations, meaning that a single publication may contribute to multiple countries when authors are affiliated with institutions in different regions.

To ensure data consistency and reliability, all extracted bibliometric data were cross-checked across tables, figures, and descriptive statistics. Any discrepancies identified during the validation process were corrected to maintain internal consistency. The full counting approach applied to country contributions was also verified to ensure alignment with the total number of publications and co-authorship structures.

3.6. Analytical Tools

Bibliometric and network analyses were conducted using R (version 4.5.1), employing dedicated bibliometric and visualization packages for performance analysis, science mapping, and network construction. The R environment enables flexible data handling and reproducible workflows, which are increasingly recommended in bibliometric and energy research [45]. The analysis generated visual representations of keyword co-occurrence networks, author collaboration structures, country collaboration patterns, and thematic clusters within the dataset.

The analytical workflow involved data preprocessing, keyword normalization, and network construction in order to ensure consistency across bibliometric indicators. Network visualizations were produced using full counting methods, where each occurrence of a keyword, author, or affiliation contributes equally to the network structure. Standardized parameter settings were applied to maintain comparability across analyses and to support transparent interpretation of the results.

This review followed the PRISMA 2020 guidelines to ensure transparency in the study selection process [44]. The review protocol was not formally registered.

3.7. Limitations of the Method

Despite its systematic design, this review is subject to certain limitations. First, the analysis is restricted to the Scopus database, which may exclude relevant studies indexed in other academic databases. Second, only English-language journal articles were considered, potentially overlooking valuable research published in other languages. Third, while bibliometric analysis is effective for identifying quantitative patterns and structural trends, it may not fully capture the qualitative depth or contextual nuances of individual studies. These limitations are acknowledged and taken into account when interpreting the findings and drawing conclusions.

4. Results

This section presents the results of the bibliometric analysis conducted on the final dataset of 595 journal publications. The analysis highlights publication growth, leading sources, collaboration patterns, and keyword structures, offering a quantitative overview of how research on artificial intelligence has evolved within the renewable energy transition. These results provide the empirical foundation for the subsequent discussion of thematic developments and research directions.

4.1. Descriptive Bibliometric Overview

The descriptive analysis summarizes key publication characteristics, leading sources, geographical contributions, institutional affiliations, keyword patterns, and annual publication dynamics, providing an overview of how AI-related research in renewable energy transition has developed over the past decade.

4.1.1. General Publication Characteristics

Table 1. Main information about the bibliometric dataset.

Description	Results
Main Information About Data (2015:2025)
Sources (Journals, Books, etc.)	231
Documents	595
Annual Growth Rate %	15.76
Document Average Age	1.45
Average citations per doc	24.51
References	5379
Document Contents
Keywords Plus (ID)	3776
Author’s Keywords (DE)	2034
Authors
Authors	2290
Authors of single-authored docs	0
Authors Collaboration
Single-authored docs	0
Co-Authors per Doc	9.09
International co-authorships %	40.17
Document Types
Article	481
Review	114

summarizes the core descriptive statistics of the dataset spanning the years 2015 to 2025. A total of 595 journal publications, provided in Appendix A, were identified across 231 academic sources, indicating a wide disciplinary spread and increasing maturity of the research field. The annual growth rate of 15.76% highlights a strong upward trajectory in scholarly interest, reflecting global momentum toward digitalization and decarbonization efforts.

With an average document age of 1.45 years, the dataset is comparatively young, suggesting that AI applications in renewable energy transition are recent yet rapidly developing research themes. The documents collectively cite 5379 references, and each article receives an average of 24.51 citations, indicating a growing academic influence.

The dataset includes 3776 Keywords Plus and 2034 author keywords, illustrating substantial thematic diversity and methodological breadth. Authorship patterns also reinforce the collaborative nature of the field: 2290 authors contributed to the publications, with no single-authored works, an average of 9.09 co-authors per paper, and an international co-authorship rate of 40.17%.

4.1.2. Annual Publication Trends

The annual progression of publications, illustrated in Figure 3, reveals a gradual rise in scholarly output during the early part of the decade, followed by a pronounced surge beginning around 2019. This acceleration coincides with several parallel developments: rapid advancements in machine learning and deep learning, increased computational infrastructure, global commitments toward net-zero pathways, and growing industry adoption of intelligent energy management systems.

The figure includes publications from 2015 to 2025. The pattern reflects a broader shift in both academic and industrial communities toward integrating AI into renewable energy planning, forecasting, control, and policy design.

4.1.3. Leading Publication Sources

Table 2. Main publication sources.

Sources	Articles
Energies	44
IEEE access	24
Energy	19
Renewable and Sustainable Energy Reviews	18
Renewable Energy	18
Sustainability (Switzerland)	17
Energy Conversion and Management	16
Energy Reports	16
Journal of Energy Storage	15
Applied energy	14

lists the most prolific journals contributing to this research area. Energies rank first with 44 articles, followed by IEEE Access and Energy. High-impact journals such as Renewable and Sustainable Energy Reviews, Renewable Energy, and Applied Energy also appear among the top contributors, demonstrating that the topic attracts attention from both broad-scope energy journals and specialized technical outlets.

The prominence of these journals highlights the field’s dual nature: technologically intensive while deeply linked to policy and system-level transition considerations.

4.1.4. Geographical Distribution of Research Output

The global distribution of contributions is presented in

Table 3. Most prominent countries by publication frequency.

Country	Frequency
China	295
India	214
Saudi Arabia	90
Malaysia	85
Spain	81
Egypt	80
UK	79
USA	72
Turkey	67
Australia	65

, where China and India appear as the most productive countries in the dataset. Their strong presence reflects rapid renewable energy deployment and significant national investment in digital energy technologies. These figures reflect the rapid deployment of renewable technologies in Asia and the strategic prioritization of AI for national energy systems. Other leading contributors include Saudi Arabia, Malaysia, Spain, Egypt, the United Kingdom, the United States, Turkey, and Australia, indicating a geographically diverse research landscape.

This global spread underscores the universal relevance of AI-enabled solutions to energy transition challenges, especially in regions undergoing rapid modernization of energy infrastructure. The reported country-level contributions reflect full counting based on author affiliations; therefore, aggregated counts may exceed the total number of publications due to multi-country collaborations.

4.1.5. Institutional Contributions and Collaboration Patterns

The distribution of institutional contributions is illustrated in Figure 4, which highlights the most active affiliations in this research domain. The figure shows that research on AI and renewable energy transition is not only internationally distributed but also concentrated in major universities and research laboratories with strong engineering and computational science capabilities.

Collaboration across institutions and regions appears to be a defining characteristic of the field, likely due to the interdisciplinary expertise required, spanning renewable energy engineering, computer science, environmental science, and data analytics.

4.1.6. Source Impact and Influence

Figure 5 displays the journals with the highest local citation impact. While Energies publishes the highest number of articles, journals such as Applied Energy, Renewable Energy, and Energy Conversion and Management show strong local influence due to their high citation density.

This pattern suggests that high-impact journals tend to publish foundational and methodological advances, while broader-scope journals host a wider variety of applied studies.

4.1.7. Keyword Frequency and Thematic Orientation

Keyword analysis provides insight into the core themes of the literature. As shown in

Table 4. Most frequently occurring keywords in the dataset.

Words	Occurrences
Machine learning	199
Renewable energies	196
Energy systems	183
Renewable energy	172
Artificial intelligence	123
Deep learning	118
Machine-learning	110
Learning systems	108
Renewable energy resources	107
Optimization	93
Alternative energy	90
Renewable energy systems	88
Wind power	86
Forecasting	84
Energy	77
Solar energy	72
Energy efficiency	60
Renewable energy system	60
Hybrid renewable energies	58
Learning algorithms	51

, frequently occurring terms include machine learning (199 occurrences), renewable energies (196), energy systems (183), Renewable energy (172) and artificial intelligence (123).

Minor variations in keyword formatting (e.g., “machine learning” and “machine-learning”) reflect differences in author-provided keywords in the bibliographic records. These variations represent the same conceptual category and are interpreted jointly in the thematic analysis.

The prevalence of keywords related to forecasting (wind power, solar energy, forecasting) indicates a strong emphasis on prediction-oriented research. Meanwhile, the recurrence of terms such as energy efficiency, hybrid renewable energies, and learning algorithms points to broad methodological integration across AI techniques and energy technologies.

The thematic richness of the field is visually represented in the word cloud in Figure 6, where larger terms signify higher frequency. The cloud highlights dominant concepts while also illustrating the interconnectedness of AI methodologies and renewable energy themes.

4.2. Network Analysis

To complement the descriptive bibliometric overview, a series of network analyses was conducted to explore structural relationships within the research community, including patterns of co-authorship, international collaboration, and keyword co-occurrence. These networks reveal how knowledge is produced, shared, and clustered within the field, offering deeper insights into the intellectual and geographical organization of research on artificial intelligence in the renewable energy transition. The following subsections examine each network in detail and discuss its implications for the evolution and maturity of the field.

4.2.1. Author Collaboration Network

The author collaboration network (Figure 7) illustrates the extent to which researchers working on AI and renewable energy transition are connected through co-authorship relationships. The network reveals a strongly collaborative research environment characterized by multi-authored publications and dense clustering. This aligns with the quantitative results from Section 3.1, where the high average number of co-authors per article (9.09) and the absence of single-authored publications already pointed to a collective mode of knowledge production.

Distinct clusters visible in the network indicate groups of researchers who frequently collaborate, often sharing methodological approaches or focusing on similar energy technologies (e.g., wind forecasting, solar PV optimization, hybrid system design). Larger nodes represent authors with higher co-authorship frequency, often serving as intellectual anchors or team leaders directing sustained research efforts in specific subfields.

The formation of several cohesive yet interconnected clusters suggests that although subfields exist within the AI–RET domain, they remain intellectually intertwined. This reflects the interdisciplinary nature of the topic, which typically requires expertise from energy engineering, computer science, optimization, and environmental systems. The collaboration patterns suggest a maturing field, where expertise is distributed, and collective research effort is essential to address complex energy challenges.

4.2.2. Country Collaboration Network

The country-level collaboration network (Figure 8) provides insight into the geographical distribution and transnational dynamics of AI–RET research. The visualization shows a strong global orientation, with several countries occupying central positions in the network. China and India, already identified as the most productive nations in Table 3, emerge as major hubs, frequently collaborating with a broad set of partner countries. Their central positions reflect significant national investments in renewable energy expansion and digitalization initiatives.

European countries, including the United Kingdom, Spain, and Germany, form another set of tightly connected nodes, often collaborating with each other and with Asian partners. This reflects Europe’s strong focus on green transition policies and innovation-driven energy research. The United States appears as a bridging node linking multiple clusters, representing its role in developing foundational AI techniques and their application to energy systems.

The density of international links (echoing the 40.17% international co-authorship rate) underscores the globalized nature of the research domain. Renewable energy transition is a transboundary issue, and AI-based solutions benefit from diverse datasets, climatic variations, and interdisciplinary viewpoints. The presence of cross-regional clusters suggests that international collaboration not only reinforces scientific productivity but also promotes methodological exchange and shared technological advancement.

4.2.3. Keyword Collaboration Network

The keyword co-occurrence network (Figure 9) illustrates the conceptual structure of the research field by mapping how frequently key terms appear together across publications. The network reveals several distinct thematic clusters, each representing a core research direction within the AI–RET domain.

One prominent cluster is centered around terms such as machine learning, deep learning, forecasting, and solar energy, suggesting that predictive modeling continues to dominate scholarly efforts. Another major cluster includes keywords like optimization, energy efficiency, hybrid renewable systems, and energy management, indicating a strong focus on AI-supported system design and operational performance improvement. A third cluster links terms associated with wind power, short-term forecasting, and time-series analysis, reflecting specialized methodological applications to variable renewable sources.

The proximity of AI-related and energy-related keywords highlights the integrative nature of the field: computational techniques are not studied in isolation but are tightly embedded within energy system challenges. Moreover, the presence of both methodological terms (e.g., learning algorithms, neural networks) and system-level terms (e.g., renewable energy resources, decarbonization) indicates a balanced focus between technical innovation and its practical implications for the energy transition.

Viewed collectively, the keyword network suggests a field characterized by thematic richness, methodological diversity, and increasing conceptual convergence. The clustering patterns offer a foundation for the thematic classification presented in Section 3.3 and help identify directions where the literature is either concentrated or emerging.

4.3. Evolution of Research Themes and Intellectual Structure

The analysis highlights how the intellectual focus of the field has evolved over time. Early studies predominantly emphasized prediction-oriented applications, particularly short-term forecasting of renewable energy generation. This trend is reflected in the high frequency of keywords such as machine learning, forecasting, wind power, and solar energy.

Over time, research attention has gradually shifted toward system-level optimization and operational efficiency. The increasing prominence of keywords such as optimization, energy systems, and energy efficiency suggests a broader systems perspective, where AI methods are applied to improve the performance of entire energy infrastructures rather than isolated components.

More recent publications increasingly explore advanced AI techniques and decision-support applications, including energy management, smart grid optimization, and integrated renewable energy systems. This progression reflects the maturation of the field and its transition toward more comprehensive AI-enabled energy solutions.

4.4. Synthesis of Bibliometric and Thematic Findings

The results indicate a rapidly growing and highly collaborative research field, supported by strong international co-authorship and contributions from a diverse set of journals, institutions, and countries.

Network analyses reveal well-connected author, country, and keyword structures, highlighting the interdisciplinary and global nature of the field. The close integration of AI methodologies with renewable energy system applications reflects a convergence of computational innovation and energy transition objectives.

Thematic patterns show a clear progression from early forecasting-focused studies toward broader system optimization, energy management, and integrative AI applications. Collectively, these findings provide a structured foundation for the subsequent discussion of theoretical implications, research gaps, and future directions.

5. Discussion

This section interprets the bibliometric and thematic findings presented in Section 3 and discusses their implications for the role of artificial intelligence (AI) in supporting the renewable energy transition (RET). The discussion also highlights methodological limitations, emerging research directions, and policy implications associated with AI-enabled energy systems.

5.1. Interpretation of Bibliometric Patterns

The strong growth in publications and the high level of international collaboration indicate that research on AI-enabled renewable energy systems has evolved from an emerging research niche into an established interdisciplinary domain. The dominance of multi-authored and cross-country publications reflects the increasing complexity of renewable energy systems and the need for collaboration across energy engineering, computer science, and data analytics.

This collaborative research structure also reflects the global nature of the renewable energy transition. Integrating large-scale renewable generation into existing energy infrastructures requires coordinated research efforts, shared datasets, and cross-institutional expertise. The observed collaboration networks therefore suggest that AI-driven renewable energy research is becoming increasingly embedded within broader international energy transition initiatives.

However, the high level of collaborative research may also influence the thematic orientation of the field. Large research consortia and funded projects often focus on technologically intensive domains such as smart grids, system optimization, and energy forecasting, potentially leaving smaller or emerging research topics underrepresented. This observation highlights the importance of maintaining methodological diversity and encouraging broader research participation.

5.2. The Role of AI in Advancing the Renewable Energy Transition

The thematic evolution identified in the bibliometric analysis indicates a gradual shift in the role of AI within renewable energy systems. Earlier studies primarily focused on forecasting renewable generation and electricity demand using machine learning and deep learning models. While forecasting remains an important research area, more recent studies increasingly emphasize system-level optimization, real-time energy management, and intelligent control of integrated energy infrastructures.

This shift reflects a broader transformation in energy systems toward digitally enabled and adaptive infrastructures. AI techniques are now being applied not only to predict renewable generation but also to support energy dispatch decisions, optimize storage utilization, and coordinate distributed energy resources. Such capabilities are essential for maintaining grid stability and reliability in systems with high shares of intermittent renewable energy.

In addition, emerging research explores hybrid approaches that combine data-driven AI models with physics-based energy system models. These hybrid architectures aim to improve prediction accuracy while maintaining physical interpretability, which is particularly important in critical energy infrastructure applications.

5.3. Methodological and Practical Challenges

Despite the rapid growth of AI applications in renewable energy systems, several methodological and practical challenges remain. A significant proportion of studies rely on simulation-based experiments rather than real-world deployments, limiting the practical validation of AI-driven energy management solutions.

Data-related challenges also remain significant. Renewable energy systems generate large volumes of heterogeneous data from sensors, smart meters, and monitoring platforms, but data availability and quality vary widely across regions and infrastructures. As a result, AI models developed in one context may not easily generalize to other climatic or operational environments.

Another key challenge concerns the transparency and interpretability of complex AI models. While deep learning architectures often provide high predictive accuracy, their limited explainability may reduce trust among system operators and policymakers responsible for managing critical infrastructure. These concerns have led to increasing interest in explainable AI (XAI) and hybrid modeling approaches designed to balance predictive performance with interpretability.

5.4. Research Gaps and Future Directions

The findings of this study highlight several promising directions for future research. First, greater attention should be devoted to hybrid modeling approaches that integrate data-driven AI techniques with physical energy system models. Such hybrid architectures have the potential to enhance predictive performance while preserving the physical interpretability required for energy system operations.

Second, future research should increasingly address system-level integration challenges associated with renewable energy deployment. While many existing studies focus on specific technologies such as solar or wind forecasting, fewer studies examine how AI can support integrated energy systems, sector coupling, and coordinated energy planning.

Third, governance and socio-technical considerations remain relatively underexplored in the literature. Issues such as data governance, cybersecurity, ethical AI deployment, and public acceptance will play an increasingly important role as AI becomes embedded within national energy infrastructures. Addressing these challenges will require interdisciplinary research that combines technical innovation with institutional and policy analysis.

5.5. Contribution to Policy and Practice

From a policy and practical perspective, the results suggest that AI has the potential to play a central role in enabling the renewable energy transition. AI-supported forecasting, optimization, and decision-support tools can improve system efficiency, enhance grid reliability, and support long-term energy planning.

However, realizing these benefits requires supportive institutional frameworks that encourage data sharing, interoperability, and responsible AI deployment. Policymakers and regulatory authorities therefore play a critical role in facilitating collaboration between researchers, industry stakeholders, and energy system operators.

Closer integration between research and practice will be essential for translating methodological advances in AI into operational improvements within renewable energy systems. Such collaboration can help ensure that AI technologies contribute not only to technical efficiency but also to broader sustainability and energy transition goals.

6. Conclusions

This study, conducted under the PRISMA framework, provides a systematic bibliometric and thematic review of research on artificial intelligence (AI) applications in the renewable energy transition (RET). By analyzing 595 peer-reviewed journal articles published between 2015 and 2025, the study maps the structural characteristics, collaborative patterns, and thematic evolution of this rapidly expanding interdisciplinary research field.

The bibliometric results demonstrate strong and sustained growth in scholarly output, accompanied by high levels of international collaboration and multi-authored research. These patterns reflect the global relevance and complexity of renewable energy transition challenges and highlight the central role of collaborative and cross-disciplinary research in advancing AI-enabled energy solutions. Network analyses further reveal a well-connected intellectual structure linking authors, countries, and thematic clusters, indicating increasing convergence between AI methodologies and renewable energy system applications.

The thematic analysis indicates a clear evolution in research focus. Early studies concentrated primarily on forecasting and prediction of renewable generation, whereas more recent studies increasingly emphasize system-level optimization, intelligent energy management, and integrated renewable energy infrastructures. This shift suggests that AI is progressively evolving from a supporting analytical tool toward a key enabling component of intelligent, adaptive, and resilient energy systems capable of managing higher shares of renewable energy.

The findings also highlight several research gaps. While AI techniques have demonstrated strong potential in forecasting and optimization, real-world deployment and system-level validation remain limited in many studies. Future research should therefore place greater emphasis on hybrid approaches combining data-driven AI models with physical energy system models, as well as on integrated energy systems, sector coupling, and scalable operational solutions. Additionally, governance-related issues—including data sharing, model transparency, cybersecurity, and ethical AI deployment—require greater attention as AI becomes more deeply embedded within energy infrastructures.

By organizing the literature through bibliometric, network, and thematic perspectives, this study provides a structured overview of how AI research contributes to the renewable energy transition. The proposed domain-wise classification and synthesis help clarify dominant research directions and emerging themes, offering useful guidance for researchers seeking to position future work in this evolving field.

Despite these contributions, several limitations should be acknowledged. The review relies on a single bibliographic database and focuses exclusively on English-language journal articles, which may exclude relevant studies published in other languages or indexed in other databases. Future reviews may therefore benefit from incorporating multiple databases, regional perspectives, and complementary qualitative analyses.

Overall, the findings confirm that artificial intelligence has become an increasingly important element of renewable energy transition research. Continued progress will depend on bridging methodological advances with real-world implementation, improving model transparency and reliability, and strengthening connections between technological innovation, energy policy frameworks, and societal needs. The insights presented in this study contribute to a better understanding of the evolving research landscape and support the continued development of AI-enabled pathways toward a sustainable energy future.