Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights
Accurate wind power prediction can improve the safety and reliability of grid operation. To further enhance the accuracy of short-term wind power prediction, this paper proposes a CNN-LSTM&GRU multi-model combined prediction method considering the difficulty in obtaining optimal prediction resul...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2022-05-01
|
| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104023 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850053044550500352 |
|---|---|
| author | Rui JIA Guohua YANG Haofeng ZHENG Honghao ZHANG Xuan LIU Hang YU |
| author_facet | Rui JIA Guohua YANG Haofeng ZHENG Honghao ZHANG Xuan LIU Hang YU |
| author_sort | Rui JIA |
| collection | DOAJ |
| description | Accurate wind power prediction can improve the safety and reliability of grid operation. To further enhance the accuracy of short-term wind power prediction, this paper proposes a CNN-LSTM&GRU multi-model combined prediction method considering the difficulty in obtaining optimal prediction results with a single model. Firstly, a convolutional neural network (CNN) is used to extract local features of data and combined with a long short-term memory (LSTM) network to construct a CNN-LSTM network structure that incorporates local feature pre-extraction modules. Then, the CNN-LSTM network is paralleled with a gated recurrent unit (GRU) network. An adaptive weight learning module is employed to select the best weights for the outputs of the CNN-LSTM module and the GRU module. In this way, the paper constructs a combined short-term prediction model based on CNN-LSTM&GRU. Finally, the model is applied to the power prediction of a wind farm in northwestern China. The experimental results show that the proposed model has a smaller mean absolute error (MAE), a smaller root mean square error (RMSE), and higher prediction accuracy than single models and other combined models. |
| format | Article |
| id | doaj-art-ce571131c49f4a1380e61ca6acf93627 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-05-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-ce571131c49f4a1380e61ca6acf936272025-08-20T02:52:38ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-05-01555475610.11930/j.issn.1004-9649.202104023zgdl-55-10-jiaruiCombined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive WeightsRui JIA0Guohua YANG1Haofeng ZHENG2Honghao ZHANG3Xuan LIU4Hang YU5School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, ChinaAccurate wind power prediction can improve the safety and reliability of grid operation. To further enhance the accuracy of short-term wind power prediction, this paper proposes a CNN-LSTM&GRU multi-model combined prediction method considering the difficulty in obtaining optimal prediction results with a single model. Firstly, a convolutional neural network (CNN) is used to extract local features of data and combined with a long short-term memory (LSTM) network to construct a CNN-LSTM network structure that incorporates local feature pre-extraction modules. Then, the CNN-LSTM network is paralleled with a gated recurrent unit (GRU) network. An adaptive weight learning module is employed to select the best weights for the outputs of the CNN-LSTM module and the GRU module. In this way, the paper constructs a combined short-term prediction model based on CNN-LSTM&GRU. Finally, the model is applied to the power prediction of a wind farm in northwestern China. The experimental results show that the proposed model has a smaller mean absolute error (MAE), a smaller root mean square error (RMSE), and higher prediction accuracy than single models and other combined models.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104023short-term wind power predictioncnn-lstmgrucombined predictionadaptive weight learning |
| spellingShingle | Rui JIA Guohua YANG Haofeng ZHENG Honghao ZHANG Xuan LIU Hang YU Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights Zhongguo dianli short-term wind power prediction cnn-lstm gru combined prediction adaptive weight learning |
| title | Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights |
| title_full | Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights |
| title_fullStr | Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights |
| title_full_unstemmed | Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights |
| title_short | Combined Wind Power Prediction Method Based on CNN-LSTM&GRU with Adaptive Weights |
| title_sort | combined wind power prediction method based on cnn lstm gru with adaptive weights |
| topic | short-term wind power prediction cnn-lstm gru combined prediction adaptive weight learning |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104023 |
| work_keys_str_mv | AT ruijia combinedwindpowerpredictionmethodbasedoncnnlstmgruwithadaptiveweights AT guohuayang combinedwindpowerpredictionmethodbasedoncnnlstmgruwithadaptiveweights AT haofengzheng combinedwindpowerpredictionmethodbasedoncnnlstmgruwithadaptiveweights AT honghaozhang combinedwindpowerpredictionmethodbasedoncnnlstmgruwithadaptiveweights AT xuanliu combinedwindpowerpredictionmethodbasedoncnnlstmgruwithadaptiveweights AT hangyu combinedwindpowerpredictionmethodbasedoncnnlstmgruwithadaptiveweights |