ML-Based Demand Forecasting: Lessons from Temporal and Weather-Driven Energy Models

Abstract

Accurate energy demand forecasting is essential for grid stability, generation planning, market operations, and renewable integration. This paper presents a practical engineering framework for ML-based energy demand forecasting that systematically incorporates temporal decomposition and weather-driven exogenous variables. Drawing on research conducted between 2020 and 2021, we evaluate multiple approaches from classical statistical methods (ARIMA, Prophet) through gradient-boosted models (XGBoost, LightGBM) to deep learning architectures (LSTM, TCN, Temporal Fusion Transformer). The framework introduces structured weather feature engineering capturing non-linear temperature-demand relationships, wind chill effects, humidity-corrected cooling demand, and solar irradiance impacts. Experimental evaluation on hourly data from two regional grids (36 months) demonstrates that weather-augmented deep learning reduces MAPE by 18.4% compared to temporal-only baselines and 31.2% compared to statistical methods. We present failure mode analysis for extreme weather, holidays, and COVID-19 structural shifts, along with production deployment strategies including uncertainty quantification, model selection heuristics, and operational monitoring.