Universiti Teknologi Malaysia Institutional Repository

An integrated data mining approach to predict electrical energy consumption

Fallahpour, Alireza and Barri, Kaveh and Wong, Kuan Yew and Jiao, Pengcheng and Alavi, Amir H. (2021) An integrated data mining approach to predict electrical energy consumption. International Journal of Bio-Inspired Computation, 17 (3). pp. 142-153. ISSN 1758-0366

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Official URL: http://dx.doi.org/10.1504/IJBIC.2021.114876

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.

Item Type:Article
Uncontrolled Keywords:ANFIS, electricity demand forecasting, feature selection, formulation
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:96144
Deposited By: Widya Wahid
Deposited On:04 Jul 2022 06:39
Last Modified:04 Jul 2022 06:39

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