Kill the Correlation, Save the Grid: Why Energy Forecasting Needs Causality
Opening — Why this matters now Energy forecasting is no longer a polite academic exercise. Grid operators are balancing volatile renewables, industrial consumers are optimizing costs under razor‑thin margins, and regulators are quietly realizing that accuracy without robustness is a liability. Yet most energy demand models still do what machine learning does best—and worst: optimize correlations and hope tomorrow looks like yesterday. This paper argues that hope is not a strategy. ...