An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load

Abstract

In the ensemble learning-based load prediction systems, recent research studies have achieved remarkable results in terms of extracting the complementary merits of multiple individual models and reducing the forecasting uncertainty, but they lack appropriate guidance for the sample-specific ensemble weighting of actual load data showing dynamics and variability. To this end, this study proposes an adaptive ensemble framework to address the above challenging and still tricky problem, and an adaptive weight-based ensemble learning model (AW-ELM) is framed to forecast short-term electric load by introducing the representative subset based weight correction with respect to the data characteristics. In the model, a weight library is formed as representatives extracting from the given training data. Based on the representatives, probability distribution modeling is built via a distance method. Then, the adaptive weights of the test data are determined by using the best matching concept. Experiments of two electric power companies in Jiangxi Province, China are implemented to evaluate the performance of the ensemble model. The results show that the ensemble model achieves better forecasting accuracy than the average combined forecasting model and the comparison models.