The Short-Term Prediction of Power Load by ARIMA Model

Abstract

Electricity load forecasting is crucial for people's daily lives. It is critical to identify an appropriate model for load prediction, as it is paramount to achieving reliable results. This article aims to explore methods for predicting short-term electricity load. The auto-regressive integrated moving average (ARIMA) model is applied to analyze the data, which consists of 2,182 consecutive hourly load values starting from 0:00 on March 1st, 2003. Four variables affecting electricity load are selected. Seasonal influences are also taken into account, and a seasonal ARIMA approach is adopted to mitigate bias caused by seasonality. To evaluate the effectiveness of the method, the Ljung-Box Q-test is performed on the residuals of the forecasted values. The results indicate that the Seasonal ARIMA model achieves the best fit, the short-term prediction is reliable. The ARIMA model only requires historical data to generate relatively accurate predictions, eliminating the need for extensive datasets, and the process is relatively simple. Overall, short-term electricity load can be explained by both historical load values and past errors.