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- W2336190013 abstract "AbstractThe demand for renewable energy is first and foremost for environmental purposes and economic purposes. Wind energy, due to its many advantages, is receiving increasing attention. Thus, to evaluate wind energy properly, an accurate forecasting method is important for solving the problem. This paper proposes a hybrid model named discrete wavelet transform (DWT)—brain storm optimization (BSO)—back propagation neural network (BPNN) to forecast short-term wind speed, which uses a DWT to decompose the original wind speed into a low-frequency and high-frequency series, remove the high frequency noise sequence and reconstruct the remaining series. Next, the brain storm optimization algorithm is used to select the best parameters of a back propagation neural network. This paper also explores how to find the best parameter choice for the proposed model. To test the stability and availability of the developed model, a three-month wind speed time series from three observation sites of a wind farm located in ..." @default.
- W2336190013 created "2016-06-24" @default.
- W2336190013 creator A5050466935 @default.
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- W2336190013 date "2017-02-01" @default.
- W2336190013 modified "2023-10-18" @default.
- W2336190013 title "Research and Application of a New Hybrid Wind Speed Forecasting Model on BSO Algorithm" @default.
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- W2336190013 doi "https://doi.org/10.1061/(asce)ey.1943-7897.0000362" @default.
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