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

• Published in: Environmental progress & sustainable energy Vol. 38; no. 1; pp. 66 - 76
• Main Authors: Torabi, Mehrnoosh, Hashemi, Sattar, Saybani, Mahmoud Reza, Shamshirband, Shahaboddin, Mosavi, Amir
• Format: Journal Article
• Language: English
• Published: 01-01-2019
• Subjects: artificial neural networks (ANN); clustering; data mining; electric energy; forecasting; support vector machine (SVM)
• ISSN: 1944-7442; 1944-7450
• DOI: 10.1002/ep.12934

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