Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation
The climate and environmental pollution problems caused by carbon dioxide and other harmful gases emitted from traditional fossil fuel thermal power plants are increasingly threatening the living environment of mankind. In September, 2020, the Chinese government clearly put forward the national stra...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722023344 |
| _version_ | 1797901906083840000 |
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| author | Hongyu Long Yongsheng He Hui Cui Qionghui Li Hao Tan Bangrui Tang |
| author_facet | Hongyu Long Yongsheng He Hui Cui Qionghui Li Hao Tan Bangrui Tang |
| author_sort | Hongyu Long |
| collection | DOAJ |
| description | The climate and environmental pollution problems caused by carbon dioxide and other harmful gases emitted from traditional fossil fuel thermal power plants are increasingly threatening the living environment of mankind. In September, 2020, the Chinese government clearly put forward the national strategic goal of “Carbon Peak and Carbon Neutrality”. Distributed generation is the main means to effectively reduce carbon emissions, especially the rapid development of wind power generation. Accurate and stable wind speed prediction can reasonably formulate power generation plans and optimize power dispatching, which is an effective means to reduce the overall carbon emissions of the power system.This paper designs and proposes a hybrid wind speed prediction model based on convolutional neural network and long short-term memory network deep learning model. Based on the historical wind speed data set collected at the location of multi-fan in the same wind farm, high-precision and stable short-term wind speed prediction is realized. Firstly, singular spectrum analysis aims to remove the noise components in the wind speed series and reduce the impact of noise on the prediction performance of the model. Secondly, convolutional neural network (CNN) is introduced to extract the features of the de-noised wind speed sequence, which provides more effective information for the training of the prediction network. Then, a prediction network based on sparrow search algorithm and long short-term memory network is constructed, and the modified sparrow search algorithm is used to optimize the selection of long short-term memory (LSTM) Hyper-parameters. Eventually, to verify the superiority of the proposed model, an evaluation system based on accuracy, stability and complexity indicators is constructed. The experimental results show that the index values of the wind speed prediction model proposed in this paper based on dataset 1 in the one-step prediction simulation experiment are the smallest among all comparison models, and the same is true for dataset 2. |
| first_indexed | 2024-04-10T09:09:22Z |
| format | Article |
| id | doaj.art-ec03fb67b66048c5b2376f33c4f323b9 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:09:22Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-ec03fb67b66048c5b2376f33c4f323b92023-02-21T05:14:22ZengElsevierEnergy Reports2352-48472022-11-0181418314199Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generationHongyu Long0Yongsheng He1Hui Cui2Qionghui Li3Hao Tan4Bangrui Tang5Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaState Grid Chongqing Electric Power Company, Chongqing 400014, ChinaChina Electric Power Research Institute, Beijing 100192, China; Corresponding author.State Grid Energy Research Institute Co., Ltd., Beijing 102209, ChinaEconomic and Technology Research Institute, State Grid Chongqing Electric Power Company, Chongqing 401120, ChinaChongqing University of Posts and Telecommunications, Chongqing 400065, ChinaThe climate and environmental pollution problems caused by carbon dioxide and other harmful gases emitted from traditional fossil fuel thermal power plants are increasingly threatening the living environment of mankind. In September, 2020, the Chinese government clearly put forward the national strategic goal of “Carbon Peak and Carbon Neutrality”. Distributed generation is the main means to effectively reduce carbon emissions, especially the rapid development of wind power generation. Accurate and stable wind speed prediction can reasonably formulate power generation plans and optimize power dispatching, which is an effective means to reduce the overall carbon emissions of the power system.This paper designs and proposes a hybrid wind speed prediction model based on convolutional neural network and long short-term memory network deep learning model. Based on the historical wind speed data set collected at the location of multi-fan in the same wind farm, high-precision and stable short-term wind speed prediction is realized. Firstly, singular spectrum analysis aims to remove the noise components in the wind speed series and reduce the impact of noise on the prediction performance of the model. Secondly, convolutional neural network (CNN) is introduced to extract the features of the de-noised wind speed sequence, which provides more effective information for the training of the prediction network. Then, a prediction network based on sparrow search algorithm and long short-term memory network is constructed, and the modified sparrow search algorithm is used to optimize the selection of long short-term memory (LSTM) Hyper-parameters. Eventually, to verify the superiority of the proposed model, an evaluation system based on accuracy, stability and complexity indicators is constructed. The experimental results show that the index values of the wind speed prediction model proposed in this paper based on dataset 1 in the one-step prediction simulation experiment are the smallest among all comparison models, and the same is true for dataset 2.http://www.sciencedirect.com/science/article/pii/S2352484722023344Short-term wind speed predictionDistributed generationConvolutional neural network (CNN)Modified sparrow search algorithm (MSSA)Deep learning model |
| spellingShingle | Hongyu Long Yongsheng He Hui Cui Qionghui Li Hao Tan Bangrui Tang Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation Energy Reports Short-term wind speed prediction Distributed generation Convolutional neural network (CNN) Modified sparrow search algorithm (MSSA) Deep learning model |
| title | Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation |
| title_full | Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation |
| title_fullStr | Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation |
| title_full_unstemmed | Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation |
| title_short | Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation |
| title_sort | research on short term wind speed prediction based on deep learning model in multi fan scenario of distributed generation |
| topic | Short-term wind speed prediction Distributed generation Convolutional neural network (CNN) Modified sparrow search algorithm (MSSA) Deep learning model |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722023344 |
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