Training-Data Generation and Incremental Testing for Daily Peak Load Forecasting

Abstract

Daily peak load forecasting (DPLF) plays a crucial role in unit commitment, security analysis, and scheduling of outages and fuel supplies in smart grid applications. Recently, various artificial intelligence-based DPLF models have been proposed for accurate electric load forecasting using sufficient datasets. However, if the available data are not sufficient for training, it is not easy to build an accurate DPLF model. Herein, we propose a novel DPLF scheme that can perform DPLF well even when the dataset for training is not sufficient. We first configured various input variables by preprocessing time and weather data, as well as the historical electric load. Then, we performed a time-series cross-validation to consider as many training datasets as possible. Simultaneously, we generated new input variables relevant to the daily peak load by using principal component analysis and factor analysis and considered them to build our DPLF model. To evaluate the performance of our approach, we employed it for day-ahead peak load forecasting and verified that our scheme can achieve better prediction performance than traditional methods.