Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Yidi Ren, Hua Li and Hsiung-Cheng Lin
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Yidi Ren: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300131, China
Hua Li: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300131, China
Hsiung-Cheng Lin: Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan

Energies, 2019, vol. 12, issue 21, 1-17

Abstract: It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

Keywords: wind speed forecasting; flower pollination algorithms; feedforward neural network; ensemble empirical mode decomposition (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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