基于改进的小波-BP神经网络的风速和风电功率预测

摘要

为了提高超短期风电功率预测精度，使用改进的小波-BP神经网络方法进行研究。针对预测模型普遍存在的延时问题，先通过离散小波变换将信号分解为高低频段的信号，再用遗传算法优化的 BP神经网络分别进行建模，最后求和各层预测信号。由于功率和风速具有混沌特性，用C-C法联合优化重构相空间的参数，以嵌入维数为神经网络输入层节点数。应用于山东某风电场，仿真结果表明，与BP神经网络模型相比，该算法预测风速和功率精度较高，但风速预测值经过实际功率曲线转换后，功率预测精度变差。%In order to improve the forecasting accuracy of ultra-short-term wind power, the improved wavelet-BP neural network method is applied. To solve the widespread delay problems of the prediction model, the original signal is decomposed into high and low frequency signal by the discrete wavelet transform. Moreover, genetic algorithm is used to optimize the BP neural network model separately. Finally, the summation of all the prediction results is gotten. As the wind speed and power series have chaos characteristics, the C-C method is used to optimize parameters of phase space reconstruction and the embedded dimension is taken as the input layer’s node number of neural network. It is applied in a wind farm, in Shandong Province, and the simulation results show that it has higher prediction accuracy than BP neural network model in forecasting wind speed and power. With the conversion of wind speed prediction results by the measured power curve, the power prediction accuracy goes bad.