Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting

Abstract

This study investigates the efficacy of various machine learning (ML) and deep learning (DL) models for short-term load forecasting (STLF) in the Electric Reliability Council of Texas (ERCOT) grid. A dual comparative approach is employed, evaluating models based on temporal features alone as well as in combination with actual and forecasted weather variables. The research emphasizes region-specific forecasting by capturing heterogeneous load patterns for ERCOT's individual weather zones and aggregating them to predict total load. Model evaluation is conducted using accuracy and bias metrics, with particular attention to high-demand months and peak load hours. The findings reveal that Generalized Additive Models (GAM) consistently outperform other models, most importantly during summer months and peak load hours.