Title: An integrated data mining approach to predict electrical energy consumption

Authors: Alireza Fallahpour; Kaveh Barri; Kuan Yew Wong; Pengcheng Jiao; Amir H. Alavi

Addresses: School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Malaysia ' Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA ' School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Malaysia ' Ocean College, Institute of Port, Coastal and Offshore Engineering, Zhejiang University, Zhoushan 316021, Zhejiang, China; Ministry of Education, Engineering Research Center of Oceanic Sensing Technology and Equipment, Zhejiang University, China ' Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

Abstract: This study proposes an integrated adaptive neuro fuzzy inference system (ANFIS) and gene expression programming (GEP) approach to predict long-term electrical energy consumption. The developed hybrid method uses ANFIS to find parameters with maximum effect on the electricity demand. Thereafter, the GEP algorithm is deployed to derive a robust mathematical model for the prediction of the electricity demand. Various statistical criteria are considered to verify the validity of the model. The predictions made by the ANFIS-GEP model are compared with those obtained by the simple GEP and hybrid artificial neural network (ANN)-ANFIS methods. The proposed ANFIS-GEP technique is more computationally efficient and accurate than GEP, and notably outperforms ANFIS-ANN.

Keywords: electricity demand forecasting; feature selection; ANFIS; gene expression programming; GEP; formulation.

DOI: 10.1504/IJBIC.2021.114876

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.3, pp.142 - 153

Received: 12 Jun 2019
Accepted: 18 Aug 2020
Published online: 10 May 2021 *

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