Short-term wind power forecasts by a synthetical similar time series data mining method

Gaiping Sun, Chuanwen Jiang, Pan Cheng, Yangyang Liu, Xu Wang, Yang Fu and Yang He

Renewable Energy, 2018, vol. 115, issue C, 575-584

Abstract: As the aggravating influence of growing wind power, wind power forecasting research becomes more important in economic operation and safety management of power system. A novel short-term wind power forecasting methodology consists of a hybrid clustering method and a wavelet based neural network is introduced. The clustering similar measure function combines the Euclidean Distance and Angle Cosine together, aims to identify the similar wind speed days which are close in space distance and have similar variance trend synthetically. Then similar daily samples as the predicting days are treated as training samples of an improved particle swarm optimization based wavelet neural network. The proposed forecasting strategy is applied to two real wind farms in China. The results demonstrate that the strategy can identify the similar time series and improve the predicting accuracy effectively, compared with some other forecasting models.

Keywords: Wind power forecasts; Hybrid clustering method; Similarity measure; Wavelet neural network (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:115:y:2018:i:c:p:575-584

DOI: 10.1016/j.renene.2017.08.071

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