{"title":"基于混合模态分解和改进优化的短期风电间隔预测","authors":"Jixuan Wang, Yifan Tang, Zengfu Xi, Yujing Wen, Kegui Wu, Yichao Li","doi":"10.1590/0001-3765202420230891","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.</p>","PeriodicalId":7776,"journal":{"name":"Anais da Academia Brasileira de Ciencias","volume":"96 4","pages":"e20230891"},"PeriodicalIF":1.1000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Short-Term Wind Power Interval Forecasting Based on Hybrid Modal Decomposition and Improved Optimization.\",\"authors\":\"Jixuan Wang, Yifan Tang, Zengfu Xi, Yujing Wen, Kegui Wu, Yichao Li\",\"doi\":\"10.1590/0001-3765202420230891\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.</p>\",\"PeriodicalId\":7776,\"journal\":{\"name\":\"Anais da Academia Brasileira de Ciencias\",\"volume\":\"96 4\",\"pages\":\"e20230891\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anais da Academia Brasileira de Ciencias\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1590/0001-3765202420230891\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais da Academia Brasileira de Ciencias","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1590/0001-3765202420230891","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Short-Term Wind Power Interval Forecasting Based on Hybrid Modal Decomposition and Improved Optimization.
Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.
期刊介绍:
The Brazilian Academy of Sciences (BAS) publishes its journal, Annals of the Brazilian Academy of Sciences (AABC, in its Brazilianportuguese acronym ), every 3 months, being the oldest journal in Brazil with conkinuous distribukion, daking back to 1929. This scienkihic journal aims to publish the advances in scienkihic research from both Brazilian and foreigner scienkists, who work in the main research centers in the whole world, always looking for excellence.
Essenkially a mulkidisciplinary journal, the AABC cover, with both reviews and original researches, the diverse areas represented in the Academy, such as Biology, Physics, Biomedical Sciences, Chemistry, Agrarian Sciences, Engineering, Mathemakics, Social, Health and Earth Sciences.