An integrated data mining approach to predict electrical energy consumption
by Alireza Fallahpour; Kaveh Barri; Kuan Yew Wong; Pengcheng Jiao; Amir H. Alavi
International Journal of Bio-Inspired Computation (IJBIC), Vol. 17, No. 3, 2021

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.

Online publication date: Mon, 10-May-2021

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