A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion
To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/15/5531 |
| _version_ | 1797442319535833088 |
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| author | Qiuhong Huang Xiao Wang |
| author_facet | Qiuhong Huang Xiao Wang |
| author_sort | Qiuhong Huang |
| collection | DOAJ |
| description | To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy may be reduced by the environment when adopting a single fusion model. Firstly, some wind speed sub-series were obtained by decomposing the original wind speed according to the wavelet packet decomposition (WPD), and the data sets formed by combining these sub-series with meteorological elements. Subsequently, the wind power components formed by wind speed decomposition are predicted through the long short-term memory neural network (LSTM), which is optimized by the improved particle swarm optimization (IPSO). Consequently, the predicting value of the final wind power was acquired by adopting the method of classified fusion to calculate the wind power components. Several case studies were carried out on the proposed model with the help of Python. It is found from those relevant results that the RMSE and MAE of the proposed model is 1.2382 and 0.8210, respectively. Moreover, the R<sup>2</sup> is 0.9952. Those simulating results show that the proposed model may be better for fitting the actual curve of wind power and has excellent predicting accuracy. |
| first_indexed | 2024-03-09T12:40:07Z |
| format | Article |
| id | doaj.art-b62072a6408548ef99dcac105a2292a1 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T12:40:07Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b62072a6408548ef99dcac105a2292a12023-11-30T22:19:29ZengMDPI AGEnergies1996-10732022-07-011515553110.3390/en15155531A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified FusionQiuhong Huang0Xiao Wang1Department of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaDepartment of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaTo improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy may be reduced by the environment when adopting a single fusion model. Firstly, some wind speed sub-series were obtained by decomposing the original wind speed according to the wavelet packet decomposition (WPD), and the data sets formed by combining these sub-series with meteorological elements. Subsequently, the wind power components formed by wind speed decomposition are predicted through the long short-term memory neural network (LSTM), which is optimized by the improved particle swarm optimization (IPSO). Consequently, the predicting value of the final wind power was acquired by adopting the method of classified fusion to calculate the wind power components. Several case studies were carried out on the proposed model with the help of Python. It is found from those relevant results that the RMSE and MAE of the proposed model is 1.2382 and 0.8210, respectively. Moreover, the R<sup>2</sup> is 0.9952. Those simulating results show that the proposed model may be better for fitting the actual curve of wind power and has excellent predicting accuracy.https://www.mdpi.com/1996-1073/15/15/5531IPSOLSTMwind power forecastclassification of the fusion patterndata fusion |
| spellingShingle | Qiuhong Huang Xiao Wang A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion Energies IPSO LSTM wind power forecast classification of the fusion pattern data fusion |
| title | A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion |
| title_full | A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion |
| title_fullStr | A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion |
| title_full_unstemmed | A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion |
| title_short | A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion |
| title_sort | forecasting model of wind power based on ipso lstm and classified fusion |
| topic | IPSO LSTM wind power forecast classification of the fusion pattern data fusion |
| url | https://www.mdpi.com/1996-1073/15/15/5531 |
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