One-Hour-Ahead Short-Term Electricity Load Forecasting Using Long Short-Term Memory Networks

Abstract

Accurate one-hour-ahead electricity load forecasting sustains dispatch, reserve planning, and dependable power-system operation, but additional inputs do not always improve predictions. This study assesses how different parameter settings contribute to power demand forecasting. Four long short-term models with a unified architecture were trained on 48048 hourly observations and a 24-hour window. Input configurations include historical demand alone, demand with twelve weather variables, demand with two calendar indicators, or all inputs. Each configuration was trained five times and evaluated on a chronological test set. The weather-based model achieved the lowest mean errors: a mean absolute percentage error of 1.599% and a root mean square error of 26.333. The history-only model remained competitive, while calendar indicators and the all-input model offered no improvement. Recent demand therefore provides most of the useful information at this horizon, with lagged weather adding a modest signal. Careful feature selection can reduce complexity and support interpretable, dependable operational forecasts.