Data-Driven Intelligence: Machine Learning Models for Short-Term Load Forecasting of a Substation Distribution Transformer

Abstract

With the increasing complexity of modern power distribution networks and the rising demand for reliable electricity, accurate load forecasting at the distribution level has become essential for efficient energy management. This study presents a performance-focused analysis of machine learning based short-term load forecasting (STLF) of a substation distribution transformer (SDT). Two STLF methods are proposed: (i) Direct load forecasting method: directly forecasts the SDT load using a dedicated SDT load model; (ii) Feeder aggregation method: forecasts individual feeder load using feeder specific load model and aggregates them with optimized weights to estimate the SDT load. Both methods perform real and reactive power forecasting with a half-hour-ahead prediction horizon and five-minute resolution. The models are trained and tested on data collected from an actual SDT and its connected feeders, ensuring real-world relevance and reliability. The proposed STLF methods offers practical insights for utility operators and system planners by supporting better informed decision-making in short-term load management.