A Hybrid clustering and classification technique for forecasting short-term energy consumption

Abstract

This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented method utilizes the clustering paradigm along with ANN and SVM approaches for accurate short-term prediction of electric energy usage, using weather data. Since the methodology being invoked in this research is based on CRISP data mining, data preparation has received a great deal of attention in this research. Once data pre-processing was done, the underlying pattern of electric energy consumption was extracted by the means of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accuracy comparing to the existing models. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 66–76, 2019.