Deep belief network based deterministic and probabilistic wind speed forecasting approach

H.Z. Wang, G.B. Wang, G.Q. Li, J.C. Peng and Y.T. Liu

Applied Energy, 2016, vol. 182, issue C, 80-93

Abstract: With the rapid growth of wind power penetration into modern power grids, wind speed forecasting (WSF) plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the wind speed time series always exhibits nonlinear and non-stationary features, making it very difficult to be predicted accurately. Recognizing this challenge, a novel deep learning based approach is proposed for deterministic and probabilistic WSF. The approach is a hybrid of wavelet transform (WT), deep belief network (DBN) and spine quantile regression (QR). WT is employed to decompose raw wind speed data into different frequency series with better behaviors. The nonlinear features and invariant structures of each frequency are completely extracted by layer-wise pre-training based DBN. Then, the uncertainties in wind speed are statistically synthesized via the QR method. Case studies using real wind farm data from China and Australia have been presented. The comparative results demonstrate that the high-level nonlinear and non-stationary feature in the wind speed series can be learned better, and competitive performance can thus be obtained. It is therefore convinced that the proposed method has a high potential for practical applications in electric power and energy systems.

Keywords: Deep belief network; Wind speed forecast; Deep learning; Quantile regression; Wavelet transform (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (112)

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DOI: 10.1016/j.apenergy.2016.08.108

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