Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting
Accurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several researc...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/8669796 |
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| author | Mohini Yadav Majid Jamil Mohammad Rizwan Richa Kapoor |
| author_facet | Mohini Yadav Majid Jamil Mohammad Rizwan Richa Kapoor |
| author_sort | Mohini Yadav |
| collection | DOAJ |
| description | Accurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several research studies being performed in this field, accurate load forecasting remains an important consideration. In this article, the design of a hybrid short-term load forecasting model (STLF) is proposed. This work combines the features of an artificial neural network (ANN), ensemble forecasting, and a deep learning network. RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. The predictions made from RBFNNs have been computed and produced as the forecast of each activated cluster. This framework is known as fuzzy-RBFNN. This proposed framework is outlined to anticipate one-week ahead load demand on an hourly basis, and its accuracy is determined using two case studies, i.e., Hellenic and Cretan power systems. Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers. |
| format | Article |
| id | doaj-art-929990a4eca2412ba9bc45f5ed01b26d |
| institution | DOAJ |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-929990a4eca2412ba9bc45f5ed01b26d2025-08-20T03:20:38ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/8669796Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load ForecastingMohini Yadav0Majid Jamil1Mohammad Rizwan2Richa Kapoor3Department of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical and ElectronicsAccurate load forecasting (LF) plays an important role in the operation and decision-making process of the power grid. Although the stochastic and nonlinear behavior of loads is highly dependent on consumer energy requirements, that demands a high level of accuracy in LF. In spite of several research studies being performed in this field, accurate load forecasting remains an important consideration. In this article, the design of a hybrid short-term load forecasting model (STLF) is proposed. This work combines the features of an artificial neural network (ANN), ensemble forecasting, and a deep learning network. RBFNNs and CNNs are trained in two phases using the functional link artificial neural network (FLANN) optimization method with a deep learning structure. The predictions made from RBFNNs have been computed and produced as the forecast of each activated cluster. This framework is known as fuzzy-RBFNN. This proposed framework is outlined to anticipate one-week ahead load demand on an hourly basis, and its accuracy is determined using two case studies, i.e., Hellenic and Cretan power systems. Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. To demonstrate the performance of RBF-CNN, SVMs replace the RBF-CNN regressor, and this model is identified as an ML-SVM having 3 layers.http://dx.doi.org/10.1155/2023/8669796 |
| spellingShingle | Mohini Yadav Majid Jamil Mohammad Rizwan Richa Kapoor Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting Journal of Electrical and Computer Engineering |
| title | Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting |
| title_full | Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting |
| title_fullStr | Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting |
| title_full_unstemmed | Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting |
| title_short | Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting |
| title_sort | application of fuzzy rbf cnn ensemble model for short term load forecasting |
| url | http://dx.doi.org/10.1155/2023/8669796 |
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