A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Chiou-Jye Huang and Ping-Huan Kuo
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Chiou-Jye Huang: School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
Ping-Huan Kuo: Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University, Pingtung 90004, Taiwan

Energies, 2018, vol. 11, issue 10, 1-20

Abstract: To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

Keywords: artificial neural network; wind speed forecasting; wind energy; power system; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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