Stratification-Based Wind Power Forecasting in a High-Penetration Wind Power System Using a Hybrid Model
| Autor: | Po-En Su, Yuan-Kang Wu, Jing-Shan Hong |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Engineering Wind power Artificial neural network business.industry 020209 energy Wind power forecasting 02 engineering and technology Industrial and Manufacturing Engineering Weighting Control and Systems Engineering Search algorithm ComputerApplications_MISCELLANEOUS 0202 electrical engineering electronic engineering information engineering Segmentation Probabilistic forecasting Electrical and Electronic Engineering business Hybrid model Physics::Atmospheric and Oceanic Physics Simulation |
| Zdroj: | IEEE Transactions on Industry Applications. 52:2016-2030 |
| ISSN: | 1939-9367 0093-9994 |
| DOI: | 10.1109/tia.2016.2524439 |
| Popis: | This paper proposes a novel stratification-based wind power forecasting method and develops a hybrid forecasting model at different stratifications 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 unstratified hybrid models in terms of forecasting accuracy, which verifies the proposed forecasting model for accurate wind power forecasting. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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