multistep wind speed forecasting based on wavelet and gaussian processes

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ID: 183497
2013
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Abstract
Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error) compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.
Reference Key
chen2013mathematicalmultistep Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Niya Chen;Zheng Qian;Xiaofeng Meng
Journal journal of power sources
Year 2013
DOI
10.1155/2013/461983
URL
http://dx.doi.org/10.1155/2013/461983
Keywords
civil engineering (general)mathematics engineering (general)

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