A Demand Forecasting Framework with Stagewise, Piecewise, and Pairwise Selection Techniques

Abstract

We propose an innovative electricity demand forecasting framework based on three model-selection techniques to maximize the forecasting accuracy. In the framework, we forecast the day-ahead electricity demand every 15 minutes based on temperature and cloud data, which are sampled from 35 weather stations. We develop three progressive techniques for selecting the model and data. First, using a stagewise forward technique, we select highly-correlated weather stations and group the best combination of selected stations. Second, using a piecewise series technique, we select the best performing forecasting machine every hour by comparing the forecasting accuracy of four forecasting machines. Third, we develop a pairwise mapping technique to combine two tandem forecasting models at the smaller sampling interval when the sampling intervals of weather and demand data differ. We verify that the framework based on three selection techniques results in higher forecasting accuracy using data from the 2018 RTE demand forecasting competition held in France.