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Short-Term Wind Power Forecasting Model based on ICA-BP Neural Network

机译:基于ICA-BP神经网络的风电短期预测模型

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It’s of great significance for wind power integration into grid to forecast wind power. Based on forecasting wind power by BP neural network, the article introduces global optimization algorithm, Imperialist Competitive Algorithm (ICA) to provide optimized initial weights of BP neural network. Thus, it can overcome the entrapment in local optical optimum of BP neural network. Compared with BP neural network, it is found that the performances of ICA-BP neural network, which are training, testing and forecasting of wind power, are much better.
机译:对于将风电集成到电网中以预测风电具有重要意义。在基于BP神经网络预测风电功率的基础上,介绍了全局优化算法-帝国主义竞争算法(ICA),为BP神经网络提供了最优的初始权重。因此,它可以克服BP神经网络在局部光学最优中的陷入。与BP神经网络相比,发现ICA-BP神经网络在风能的训练,测试和预测方面的性能要好得多。

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