Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method
Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-...
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MDPI AG
2017-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/8/1196 |
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doaj-d78b08358bfb4ff6944c1119c12433812020-11-24T20:42:05ZengMDPI AGEnergies1996-10732017-08-01108119610.3390/en10081196en10081196Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination MethodDongxiao Niu0Yi Liang1Haichao Wang2Meng Wang3Wei-Chiang Hong4School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Education Intelligent Technology, Jiangsu Normal University, Xuzhou 221116, ChinaStable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid.https://www.mdpi.com/1996-1073/10/8/1196icing forecastingback propagation neural networkmind evolutionary computationbat algorithmsupport vector machineextreme learning machine with kernelvariance-covariance |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Dongxiao Niu Yi Liang Haichao Wang Meng Wang Wei-Chiang Hong |
| spellingShingle |
Dongxiao Niu Yi Liang Haichao Wang Meng Wang Wei-Chiang Hong Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method Energies icing forecasting back propagation neural network mind evolutionary computation bat algorithm support vector machine extreme learning machine with kernel variance-covariance |
| author_facet |
Dongxiao Niu Yi Liang Haichao Wang Meng Wang Wei-Chiang Hong |
| author_sort |
Dongxiao Niu |
| title |
Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method |
| title_short |
Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method |
| title_full |
Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method |
| title_fullStr |
Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method |
| title_full_unstemmed |
Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method |
| title_sort |
icing forecasting of transmission lines with a modified back propagation neural network-support vector machine-extreme learning machine with kernel (bpnn-svm-kelm) based on the variance-covariance weight determination method |
| publisher |
MDPI AG |
| series |
Energies |
| issn |
1996-1073 |
| publishDate |
2017-08-01 |
| description |
Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid. |
| topic |
icing forecasting back propagation neural network mind evolutionary computation bat algorithm support vector machine extreme learning machine with kernel variance-covariance |
| url |
https://www.mdpi.com/1996-1073/10/8/1196 |
| work_keys_str_mv |
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