Machine learning and artificial intelligence are transforming energy forecasting. Recent research demonstrates that neural network architectures — including hybrid sigmoid–neural models (HNSM) and ensemble approaches (ENSM) — achieve R² above 0.97 for long-horizon renewable capacity forecasting, outperforming traditional statistical methods by 35% or more.
Forecasting models — LSTM and transformer-based models are widely used for short-term demand and price forecasting. For long-horizon capacity (e.g., 2025–2050), S-curve and hybrid neural–sigmoid models capture structural dynamics more effectively than pure time-series approaches.
Reinforcement learning — Multi-agent reinforcement learning (MAPPO, PPO, SAC) is being applied to energy policy optimisation and scenario exploration. Researchers are using RL to identify optimal renewable deployment pathways under multiple objectives and constraints.
Integration — The combination of forecasting and RL enables end-to-end energy transition modelling: forecast future capacity, then optimise policy levers to achieve targets while minimising cost and emissions. This platform's research implements such an integrated pipeline.