This work is like a detailed map of how AI is being used across the renewable energy world – solar, wind, storage, grids – showing who is doing what, which ideas are hot, and where new opportunities are opening up.
Energy executives struggle to see the full landscape of how AI is actually being applied in renewables (grid optimization, forecasting, predictive maintenance, trading, etc.). This study organizes the scientific literature to highlight key application areas, trends, and gaps so leaders can prioritize investments and R&D partnerships instead of guessing.
Curated, structured view of the scientific literature around AI in renewable energy (domain-specific knowledge graph of papers, topics, and trends), which can be embedded into internal strategy workflows and updated over time.
Classical-ML (Scikit/XGBoost)
Unknown
Medium (Integration logic)
Literature coverage and update frequency – the value depends on continuously ingesting and re-mapping a rapidly growing body of AI & energy publications.
Early Majority
Rather than being a point solution (e.g., a single forecasting model), this is a mapping and meta-analysis of how AI is used across the renewable energy sector, supporting portfolio-level decisions on where and how to apply AI.