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• W2202904384 abstract "This work proposes a novel stratification-based wind power forecasting method, and develops a hybrid forecasting model at different stratifications by using charged system search algorithm. The proposed model applies the concept of segmentation from the theory of optimal stratification to forecast short-term wind power outputs. Additionally, the proposed method elucidates different weighting values of each individual model at different segmentation blocks. Based on the forecasting results, the proposed stratification-based hybrid model outperforms traditional stand-alone models and un-stratified hybrid models in terms of forecasting accuracy, which verifies the proposed forecasting model for accurate wind power forecasting." @default.
• W2202904384 created "2016-06-24" @default.
• W2202904384 creator A5051019311 @default.
• W2202904384 creator A5056462505 @default.
• W2202904384 creator A5058622539 @default.
• W2202904384 date "2015-10-01" @default.
• W2202904384 modified "2023-09-28" @default.
• W2202904384 title "Stratification-based wind power forecasting in a high penetration wind power system using a hybrid model with charged system search algorithm" @default.
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• W2202904384 doi "https://doi.org/10.1109/ias.2015.7356793" @default.
• W2202904384 hasPublicationYear "2015" @default.
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