Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days
With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term predic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/8/4/80 |
| _version_ | 1797547359560794112 |
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| author | Yalong Li Fan Yang Wenting Zha Licheng Yan |
| author_facet | Yalong Li Fan Yang Wenting Zha Licheng Yan |
| author_sort | Yalong Li |
| collection | DOAJ |
| description | With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method. |
| first_indexed | 2024-03-10T14:43:10Z |
| format | Article |
| id | doaj.art-b3cc564e285447b1850740506f94da54 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T14:43:10Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-b3cc564e285447b1850740506f94da542023-11-20T21:39:38ZengMDPI AGMachines2075-17022020-11-01848010.3390/machines8040080Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar DaysYalong Li0Fan Yang1Wenting Zha2Licheng Yan3School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaWith the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.https://www.mdpi.com/2075-1702/8/4/80wind power predictioncombination modelconvolutional neural networksimilar daysoptimization algorithm |
| spellingShingle | Yalong Li Fan Yang Wenting Zha Licheng Yan Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days Machines wind power prediction combination model convolutional neural network similar days optimization algorithm |
| title | Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days |
| title_full | Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days |
| title_fullStr | Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days |
| title_full_unstemmed | Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days |
| title_short | Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days |
| title_sort | combined optimization prediction model of regional wind power based on convolution neural network and similar days |
| topic | wind power prediction combination model convolutional neural network similar days optimization algorithm |
| url | https://www.mdpi.com/2075-1702/8/4/80 |
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