基于PCC-RBF网络的风电功率短期预测方法

风电功率预测对风电场安全平稳运行、电网调度具有重要意义。针对风电功率短期预测指标选择不合理、预测精确度偏低的问题,提出一种基于皮尔逊相关系数(PCC)和径向基函数(RBF)神经网络的风电功率短期预测方法。该方法利用PCC筛选出与风电功率密切相关的3个指标,即电流、温度、风速,然后以这3个指标作为预测模型的输入对风电功率进行RBF样本训练与短期预测。试验结果表明,所提的预测模型预测误差更小,预测精度更高,能够满足风电功率短期预测的要求,具有广泛的应用前景。

Short-Term Wind Power Forecast Method Based on Pearson Correlation Coefficient and RBF Network

Wind power forecast is of great significance for the safe and stable operation of wind farms and power grid dispatching. At present, the selection of wind power short term forecast indicators is unreasonable and the forecast accuracy is low. Aiming at these problems, a short term wind power forecast model based on Pearson correlation coefficient (PCC) and radial basis function (RBF) neural network is proposed. Firstly, three indicators closely related to wind power, e.g., current, temperature and wind speed, are selected by PCC. Then, these three indicators are used as the input of the forecast model for RBF samples training and short term forecast of wind power. The results show that the proposed forecast model has smaller forecast error and higher prediction accuracy. It can meet the requirements of short term wind power prediction and has a wide application prospect.