A Hybrid clustering and classification technique for forecasting short‐term energy consumption. Issue 1 (27th June 2018)

• Record Type: Journal Article
• Authors: Torabi, Mehrnoosh; Hashemi, Sattar; Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Mosavi, Amir
• Is Part Of: Environmental progress & sustainable energy. Volume 38:Issue 1(2019)
• Page Start: 66
• Page End: 76
• Publication Date: 2018-06-27
• Subjects: support vector machine (SVM) -- artificial neural networks (ANN) -- electric energy -- forecasting -- clustering -- data mining; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.12934
• Languages: English
• ISSNs: 1944-7442

Abstract

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