Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization
Short-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/16/4890 |
| _version_ | 1797524056990285824 |
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| author | Mengran Zhou Tianyu Hu Kai Bian Wenhao Lai Feng Hu Oumaima Hamrani Ziwei Zhu |
| author_facet | Mengran Zhou Tianyu Hu Kai Bian Wenhao Lai Feng Hu Oumaima Hamrani Ziwei Zhu |
| author_sort | Mengran Zhou |
| collection | DOAJ |
| description | Short-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R<sup>2</sup> is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting. |
| first_indexed | 2024-03-10T08:51:55Z |
| format | Article |
| id | doaj.art-ac3eebe0df174f87b6cf6d5ce002debe |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:51:55Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ac3eebe0df174f87b6cf6d5ce002debe2023-11-22T07:28:54ZengMDPI AGEnergies1996-10732021-08-011416489010.3390/en14164890Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf OptimizationMengran Zhou0Tianyu Hu1Kai Bian2Wenhao Lai3Feng Hu4Oumaima Hamrani5Ziwei Zhu6School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaShort-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R<sup>2</sup> is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.https://www.mdpi.com/1996-1073/14/16/4890electric load forecastingload seriesvariational mode decompositiongrey wolf optimizationsupport vector regression |
| spellingShingle | Mengran Zhou Tianyu Hu Kai Bian Wenhao Lai Feng Hu Oumaima Hamrani Ziwei Zhu Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization Energies electric load forecasting load series variational mode decomposition grey wolf optimization support vector regression |
| title | Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization |
| title_full | Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization |
| title_fullStr | Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization |
| title_full_unstemmed | Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization |
| title_short | Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization |
| title_sort | short term electric load forecasting based on variational mode decomposition and grey wolf optimization |
| topic | electric load forecasting load series variational mode decomposition grey wolf optimization support vector regression |
| url | https://www.mdpi.com/1996-1073/14/16/4890 |
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