基于神经模糊网络的短期风电预测

Short Term Wind Power Forecasting Based on Neural Fuzzy Network

本文利用中尺度NWP模型,以半小时为间隔,对风电场参考点未来36小时的气象变量进行了预报,提出了一种基于神经网络和模糊逻辑相结合的混合计算智能技术的风力发电预测统计模型。利用该统计模型对NWP模型输出、SCADA和风塔的实测数据进行处理,准确预测风电场中各风机的风电功率。同时介绍了网络结构和训练算法,并将该预测方法应用于我国某实际风电场的风电预测。预测风电与实际风电之间的均方根误差(RMSE)小于20%。预测结果表明,训练后的神经模糊网络对风电场建模和风电预测具有较强的应用价值。由于神经模糊网络的适应性,该方法可集成到在线风电预测系统中,在运行过程中自动调整。

This paper presents a statistical model based on a hybrid computational intelligence technique that merging neuralnetworks and fuzzy logic for wind power forecasting.A mesoscale NWP model is used to forecast meteorological variables at a reference point of a wind farm for the next 36 hours at half-hour intervals.The output of the NWP model, together with measured data form SCADA and wind tower, is processed by the proposed model to accurately forecast the wind power of each wind turbine in the wind farm.The network architecture and the training algorithm are introduced.The forecasting approach is applied for the wind power forecasting of a real wind farm located in China.The root mean square errors(RMSE)between the forecasted wind power and actual wind power are less than 20%.From the forecasting results obtained, we conclude: The trained neuro-fuzzy networks are powerful for modeling the wind farm and forecasting the wind power.Due to the adaptability of neuro-fuzzy networks, the proposed approach can be integrated into an on-line wind power forecasting system that automatically be tuned during operation.