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- W4312708461 abstract "Wind power prediction plays a crucial role in renewable integrated smart grid. Efficient and accurate prediction method will provide significant contribution to improve the power scheduling under a safe and stable operation. Backpropagation (BP) neural network is regarded as one of efficient artificial neural network (ANN) for prediction. However, its performance highly relies on the network structure and the weights. In order to improve the performance of an prediction accuracy, a fusion method by combining ANN and genetic algorithm (GA) is developed to predict intermittent wind power. The network weights and thresholds of a BP neural network is optimized by GA approach. Thus, the trained BP network is applied to predict wind power. In order to verify the effectiveness of the proposed method, the key indicators, including average percentage error (APE), root mean square error (RMSR), accuracy, pass rate and entropy are used to analyze the test results. The results show that the prediction accuracy and the pass rate are improved by 10.94% and 9.0%, respectively. Moreover, APE and RMSR are reduced by 5.62 and 1.98, respectively." @default.
- W4312708461 created "2023-01-05" @default.
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- W4312708461 date "2022-01-01" @default.
- W4312708461 modified "2023-10-16" @default.
- W4312708461 title "An Artificial Intelligence-Based Fusion Method for Wind Power Prediction" @default.
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- W4312708461 doi "https://doi.org/10.1007/978-981-19-3171-0_51" @default.
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