Day-Ahead Electricity Load Forecasting with Multivariate Time Series

Abstract

The importance of day-ahead load forecasting cannot be overstated. Electricity load forecasting is highly important because it allows electric distribution utilities to increase their transmission efficiency and their revenues, increase the reliability of power supply, and correct decisions for future developments. In this paper, day-ahead load forecasting was studied using multivariate time series analysis. Traditional forecasting method such as seasonal autoregressive integrated moving average (SARIMA) was used. Machine learning algorithms such as Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) were also utilized to determine their applicability to short-term load forecasting. For SARIMA, only historical hourly load was needed while FFNN and LSTM required the addition of temperature data, hour of the day and day of the week, special events and previous hour load. In the prediction, FFNN and LSTM performed better with mean absolute percentage error (MAPE) of 1.80 and 1.75%, respectively, compared with SARIMA with a MAPE of 4.48%. The study demonstrated that machine learning like FFNN and LSTM outperformed the traditional SARIMA models, highlighting their effectiveness in applications such as short-term load forecasting in time series prediction.