AI in Renewable Energy Optimization

Abstract

The energy transition from fossil fuels to renewable sources -- solar, wind, hydropower, and storage -- introduces operational complexity that conventional energy management cannot handle efficiently. Renewables are intermittent, weather-dependent, and geographically distributed; their integration into power grids requires real-time forecasting, autonomous dispatch optimisation, and demand-response coordination at scales and speeds that exceed human operator capability. AI offers four critical capabilities: probabilistic renewable generation forecasting, reinforcement learning-based energy dispatch optimisation, predictive maintenance for wind and solar assets, and demand-side flexibility management. This study evaluates nine AI systems across five energy optimisation tasks: solar irradiance forecasting, wind power forecasting, battery storage dispatch optimisation, wind turbine predictive maintenance, and grid-level demand response. Systems include WaveNet-Solar, ClimaX-Wind, TFT-Energy (Temporal Fusion Transformer adapted for energy), DRL-Dispatch (deep RL for battery dispatch), GNN-Grid (graph neural network for grid topology), XGBoost-Maint (predictive maintenance), LSTM-Demand, an LLM-powered grid operator assistant (GridGPT), and a multi-task ensemble. Data from 284 renewable energy installations across six European countries (2019-2023) and the Nordic power grid operator (Svenska kraftnat) collaboration were used. TFT-Energy achieves solar irradiance RMSE 14.2% below physical model baselines. DRL-Dispatch reduces battery cycling costs by 22.4% vs. rule-based dispatch. XGBoost-Maint detects wind turbine failures 18.4 days earlier than scheduled maintenance inspection. GNN-Grid reduces grid congestion costs by 16.8% in Nordic grid simulations. A sector-ready AI deployment roadmap for renewable energy operators is proposed.