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 |
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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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