文章基本信息

标题：Optimal Component IGSCV-SVR Ensemble Model Improved by VMD for Ultra-short-term Wind Speed Forecasting
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作者：Yu Ye ; Jinxing Che ; Heping Wang 等
期刊名称：Engineering Letters
印刷版ISSN：1816-093X
电子版ISSN：1816-0948
出版年度：2022
卷号：30
期号：3
页码：1166-1175
语种：English
出版社：Newswood Ltd
摘要：The chaotic nature of wind speed will damage power system seriously, and cause economic losses. Therefore, timely wind prediction is crucial for the safety of power system. However, the traditional prediction method is hard to fully learn the characteristic of wind speed. This paper proposes an optimal component IGSCV-SVR ensemble model to predict ultra-short-term wind speed. It changes the traditional single parameter optimization method of time series prediction. Firstly, the VMD based component correlation is applied to decomposing the original wind speed dataset to obtain multiple subsequences. Our model can find the dissimilarity of each subsequence, and then the model fully learns the feature of each subsequence. It can help improve the overall efficiency of ultra-short-term wind speed prediction accuracy. Finally, estimates are obtained by summing the prediction of all components. The case study proves the feasibility of our method through the comparative experiments with some previous prediction models in MSE, MAE, MAPE and running time in the experimental part of this paper.
关键词：ultra-short-term wind speed prediction;Decomposition;Improved grid search cross-validation;Support vector regression;component correlation