An integrated data mining approach to predict electrical energy consumption

• Main Authors: Fallahpour, Alireza, Barri, Kaveh, Wong, Kuan Yew, Jiao, Pengcheng, Alavi, Amir H.
• Format: Article
• Published: Inderscience Publishers 2021
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/id/eprint/96144/; http://dx.doi.org/10.1504/IJBIC.2021.114876

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.