Comparison of Machine Learning Models for Week-Ahead Load Forecasting in Short-term Power System Planning

This study examines the application of machine learning models to week-ahead load forecasting for power system short-term planning applications. Electrical energy consumption has increased significantly over the last two decades because of rising living standards, economic development, and population growth. For power system planners and operators, short-term load forecasting is necessary to make economic, safe, reliable, and operational planning decisions and to account for variations in energy demand based on the seasonal period. These models are essential for the operation of utilities to meet system short-term adequacy assessment studies. With uncertainties in national, regional, and local economic growth, along with seasonal variability in energy consumption patterns and conservation habits of electricity consumers, utilities may face new challenges in forecasting short-term load demand. This work considers five machine learning models for week-ahead hourly load forecasting based on historical actual load data and expected meteorological features such as humidity, pressure, cloud cover, temperature, and wind speed. The performances of the models are evaluated using real-world data provided on the IEEE DataPort for Electricity Demand Forecasting. The accuracies are assessed using the root mean square error (RMSE) and coefficient of determination (R
²
) metrics. The results show that the Nonlinear autoregressive neural network with exogenous input (NARX) model performs well in predicting week-ahead load demand with an R
²
value of 0.99 on the test set and can accommodate seasonal trends in energy consumption.