基于风光混合模型的短期功率预测方法研究

准确地预测风力发电及光伏发电的输出功率对提高风光互补供电系统的调度质量具有重要意义。建立了基于BP神经网络的风光混合预测模型,将现有技术中分两次预测的风电功率和光伏功率采用同一个预测模型,同时实现整个区域风电场及光伏电站的输出功率预测,在简化预测方法的同时提高预测准确度。通过某海岛的风电及光伏电站的实际数据验证,计算分析了预测误差。结果表明该方法具有较高的预测精度,对风光混合的功率预测具有一定的学术价值和工程实用价值。

Short term power prediction based on hybrid wind-PV forecasting model

Accurate wind power and photovoltaic power outputs forecasting is important to improve the scheduling quality of wind and solar hybrid generation system. The hybrid wind-PV forecasting model based on BP neural network is established to achieve the forecasting of the entire wind power and photovoltaic power station, using the same forecasting model instead of twice forecasting, it can simplify the prediction and improve the prediction precision effectively. The method is validated by wind farm and photovoltaic system data in a coastal islands and the forecast error is calculated and analyzed. The results show the method has high accuracy, which has good academic value and practical value to forecast power for hybrid wind-PV generation system.