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Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators
Open AccessArticle

Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine

1
Department of Electrical Engineering, Semnan University, Semnan 35195-363, Iran
2
Department of Electrical Engineering, Budapest University of Technology and Economics, 1052 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Sustainability 2017, 9(11), 2104; https://doi.org/10.3390/su9112104
Received: 30 September 2017 / Revised: 9 November 2017 / Accepted: 10 November 2017 / Published: 16 November 2017
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
The growing trend of wind generation in power systems and its uncertain nature have recently highlighted the importance of wind power prediction. In this paper a new wind power prediction approach is proposed which includes an improved version of Kriging Interpolation Method (KIM), Empirical Mode Decomposition (EMD), an information-theoretic feature selection method, and a closed-loop forecasting engine. In the proposed approach, EMD decomposes volatile wind power time series into more smooth and well-behaved components. To enhance the performance of EMD, Improved KIM (IKIM) is used instead of Cubic Spline (CS) fitting in it. The proposed IKIM includes the von Karman covariance model whose settings are optimized based on error variance minimization using an evolutionary algorithm. Each component obtained by this EMD decomposition is separately predicted by a closed-loop neural network-based forecasting engine whose inputs are determined by an information-theoretic feature selection method. Wind power prediction results are obtained by combining all individual forecasts of these components. The proposed wind power forecast approach is tested on the real-world wind farms in Spain and Alberta, Canada. The results obtained from the proposed approach are extensively compared with the results of many other wind power prediction methods. View Full-Text
Keywords: wind power prediction; Empirical Mode Decomposition (EMD); Kriging Interpolation Method (KIM); Neural Network (NN); feature selection method; closed-loop forecasting engine wind power prediction; Empirical Mode Decomposition (EMD); Kriging Interpolation Method (KIM); Neural Network (NN); feature selection method; closed-loop forecasting engine
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Amjady, N.; Abedinia, O. Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine. Sustainability 2017, 9, 2104.

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