基于NWP和深度学习神经网络短期风功率预测

Short-term wind power forecasting based on NWP and deep learning neural network

对风电场的准确预测,可以为电网调度提供调峰和消纳依据,从而综合评估电网短期内消纳风电的能力,制定科学合理的消纳措施。通过预测风电场24 h内的出力,基于数值天气预报(NWP)数据的出力预测,采用深度学习神经网络算法,建立数值天气预报与风电功率之间的转换模型,计算功率点预测值,然后利用概率密度函数,建立风电出力预测的概率区间。最后通过实际案例仿真,验证了基于NWP和深度学习神经网络短期风功率预测的可靠性,为调度预留调峰容量提供理论依据。

The accurate prediction of wind power plant can provide the basis for peak load regulation and absorption of power grid dispatching,so as to comprehensively evaluate the power grid′ s short-term absorption capacity of wind power,and formulate scientific and reasonable absorption measures. The conversion model between numerical weather forecast(NWP)and wind power is established based on the output′s prediction of wind power within 24 hours,the NWP data and deep learning neural network algorithm,the power point prediction value is calculated,and then the probability interval of the wind power output prediction is established by means of the probability density function. The reliability of short-term wind power prediction based on NWP and deep learning neural network is verified by the simulation of practical cases,which provides the theoretical basis for scheduling and reserving peak load regulating capacity.