Short-term wind speed forecasting based on an EEMD-CAPSO-RVM model

Zang, Haixiang; Liang, Zhi Jie; Mian, Guo; Qian, Zeyu; Wei, Zhinong; Sun, Guoqiang

doi:10.1109/appeec.2016.7779542

Cited by 2 publications

(1 citation statement)

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“…In this paper, critical success index C SI , root mean square error λ rmse , mean absolute percentage error λ mape are used to evaluate the proposed prediction model's performance (Zang et al, 2016;Mi et al, 2017). The formulas are as follow:…”

Section: Performance Evaluationmentioning

confidence: 99%

Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

et al. 2022

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Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.

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Section: Performance Evaluationmentioning

confidence: 99%