论文标题
风力坡道预测算法基于小波深度信念网络
Wind power ramp prediction algorithm based on wavelet deep belief network
论文作者
论文摘要
风力坡道事件极大地威胁了电网安全性。为了提高坡道的预测准确性，提出了具有自适应特征选择（WDBNAF）的混合小波深度信念网络算法。首先，分析了风能特征。然后，将小波分解用于时间序列，并提出了一种自适应特征选择算法来选择预测模型的输入。最后，采用了深层信念网络来预测风力坡道事件，并根据实际数据通过实验证明了拟议的WDBNAFS。仿真结果表明，所提出的算法的预测准确性超过90％。
The wind power ramp events threaten the power grid safety significantly. To improve the ramp prediction accuracy, a hybrid wavelet deep belief network algorithm with adaptive feature selection (WDBNAFS) is proposed. First, the wind power characteristic is analyzed. Then, wavelet decomposition is addressed to the time series, and an adaptive feature selection algorithm is proposed to select the inputs of the prediction model. Finally, a deep belief network is employed to predict the wind power ramp event, and the proposed WDBNAFS was testified with the experiments based on the practical data. The simulation results demonstrate that the prediction accuracy of the proposed algorithm is more than 90%.
