ML in Flexible Energy Systems

Forecasting and Optimisation in Flexible Energy Systems

Forecasting provides predictive visibility into demand, renewable generation, price signals, and asset availability. Machine learning models continuously update short-term predictions as new telemetry arrives, enabling proactive rather than reactive balancing.

AI-driven Demand Response systems use these forecasts to coordinate distributed assets at scale. Small adjustments across many buildings — such as marginal thermostat changes during constrained periods — can aggregate into significant system-level load reductions.

Optimisation translates forecasts into economic and operational decisions. Given predicted conditions and constraints, algorithms determine which assets to dispatch, curtail, or ramp. These problems typically involve cost minimisation or revenue maximisation subject to physical and contractual limits. Optimisation operationalises flexibility in both technical and market terms.

Responsible AI in Flexible Energy Systems

Energy systems are critical infrastructure. AI models used for forecasting and dispatch must prioritise explainability, fairness, security, and robust governance. Transparent decision frameworks build trust and reduce systemic risk, particularly where automated control actions have real-world operational consequences.