An integrated data mining approach to predict electrical energy consumption
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Inderscience Publishers
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/96144/ http://dx.doi.org/10.1504/IJBIC.2021.114876 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | 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. |
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