The rapid adoption of low-carbon technologies is dismantling the predictability that once defined residential energy demand, leaving utilities to navigate behind-the-meter volatility, and increasingly variable renewable generation.
Traditional forecasting models, built on historical averages, are no longer sufficient in a world where apparently identical households can behave entirely differently from one day to the next.
The path forward lies in real-time, device-level visibility and AI-driven forecasting; systems that learn continuously, optimize consumption, and integrate directly with trading operations.
This short report explores the challenge of forecasting a sector in flux- and how smart grid AI / machine learning is coming to the rescue.
