Figure 1
PRISMA Flow Diagram depicting the study identification, screening, eligibility, and inclusion process used in this systematic review.
Figure 2
IEEE Xplore Advanced Search interface showing the Boolean query used to retrieve literature related to Artificial Intelligence (AI) techniques and renewable energy domains. The search was restricted to publications from 2015 to 2025.
Figure 3
Example of search results returned from IEEE Xplore using the specified Boolean string. 22,996 records were retrieved before screening, filtered by publication year and content type.
Table 1
Inclusion and Exclusion Criteria.
| CRITERIA TYPE | DESCRIPTION |
|---|---|
| Inclusion Criteria | – Studies applying AI methods to renewable energy domains |
| – Peer-reviewed journal articles | |
| – Published between 2015 and 2025 | |
| – Full-text available in English | |
| Exclusion Criteria | – Conference papers, editorials, and non-methodological reviews |
| – Studies not applying AI or not focused on renewable energy | |
| – Articles published in languages other than English | |
| – Duplicate records across multiple databases (e.g., IEEE Xplore, ScienceDirect) |
Table 2
Example Screening Table.
| ARTICLE TITLE | ABSTRACT SUMMARY | SCREENING DECISION | JUSTIFICATION |
|---|---|---|---|
| ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction (Awachat, Dube and Chaudhri, 2025). | Explores machine learning applications in environmental sustainability, emphasizing AI’s role in smart energy and climate systems. | Included | Relevant AI techniques applied in sustainable and energy domains; falls within scope. |
| Machine Learning for Sustainable Energy Systems (Donti and Kolter 2021). | Reviews various machine learning models for predicting and optimizing solar, wind, hydropower, and bioenergy systems. | Included | Directly aligns with the paper’s objective of surveying AI applications in renewable energy. |
| Solar Energy Forecasting Using Deep Learning Techniques (Machina, Koduru andMadichetty, 2022). | Investigates deep learning methods for solar irradiance forecasting using historical data and meteorological variables. | Included | A targeted study applying deep learning in solar forecasting—a key area in AI-driven energy research. |
Figure 4
Frequency of Articles by Renewable Energy Theme – IEEE Xplore.
Figure 5
Frequency of Articles by Renewable Energy Theme – Science Direct.
Figure 6
Annual Trends of AI-Related Publications in Solar Energy (2015–2025) from IEEE Xplore and ScienceDirect.
Figure 7
Heatmap Showing the Frequency of AI Techniques Used in Solar Energy Research Across IEEE Xplore and ScienceDirect (2015–2025).
Figure 8
Annual Publication Trends of AI Applications in Solar Energy (2015–2025) by Source.
Figure 9
Emerging Trends in AI Applications for Solar Energy (2015–2025).
Figure 10
Number of AI-Related Wind Energy Articles by Year (2015–2025) from IEEE Xplore and ScienceDirect.
Figure 11
Distribution of AI Techniques Used in Wind Energy Research Across IEEE Xplore and ScienceDirect (2015–2025).
Figure 12
Annual Trend of AI-Related Publications in Wind Energy Research (2015–2025) by Source.
Figure 13
Temporal Trends of Emerging AI Techniques in Wind Energy Research (2015–2025).
Figure 14
Annual Trends of AI-Related Publications in Energy Storage and Smart Grids (2015–2025) from IEEE Xplore and ScienceDirect.
Figure 15
Heatmap of AI Techniques in Energy Storage and Smart Grids by Source (Observed Counts).
Figure 16
Yearly Trend of AI-Related Publications in Energy Storage and Smart Grids (2015–2025) Based on IEEE Xplore and ScienceDirect.
Figure 17
Emerging Trends of AI Techniques in Energy Storage and Smart Grids Research (2015–2025).
Table 3
Summary of Emerging AI Trends (2015–2025).
| AI TECHNIQUE | SOLAR ENERGY | WIND ENERGY | ENERGY STORAGE & SMART GRIDS |
|---|---|---|---|
| Machine Learning (ML) | Most dominant; used for forecasting, PV performance optimization | Widespread for wind speed prediction, control, and anomaly detection | Key technique in grid optimization, energy demand prediction |
| Deep Learning (DL) | Emerging post-2020; spike seen in 2023–2024 | Strong uptake post-2020; widely used for spatial-temporal modeling | Rapid growth post-2021 due to IoT & smart meter data |
| Reinforcement Learning | Still emerging, but visible after 2021 | More active than Solar; used in adaptive turbine control | Growing rapidly for real-time control in dynamic grid environments |
| Fuzzy Logic | Present but less dominant; popular in early years | Strong in IEEE Xplore; used in rule-based turbine control | Frequently used in decision-making and load balancing systems |
| Explainable AI (XAI) | Gaining attention 2022–2025; tied to hybrid systems | Emerging post-2022, limited but increasing focus on transparency | Present in recent years, key for interpretability in smart systems |
| Generative AI | Very limited use | Rare, but a few exploratory studies | Rare, emerging mostly post-2023 in hybrid modeling frameworks |
| Graph Neural Networks | Sparse usage, some hybrid works emerging | Slight increase post-2021 for turbine networks | Slight growth post-2022 for grid topology analysis |
| PINNs (Physics-Informed Neural Networks) | Rare application | Emerging use in hybrid models post-2023 | Notable application in physics-based grid modeling, mostly in IEEE Xplore |
| Not Specified | Still large proportion, especially 2020–2023 | Very high in ScienceDirect, suggesting poor methodological reporting | The highest volume of “unspecified” label, indicating the urgent need for clearer documentation |