摘要
在光伏发电领域,面对复杂多变的天气,实现光伏发电量的准确预测是一个难点问题,光伏发电量的准确预测能为电网平稳调度提供参考依据。根据天气气候的变化特性以及GRNN神经网络的特性,提出依据历史天气数据训练GRNN神经网络预测光伏发电量的方法,仿真结果证实,在不区分相似日的情况下,GRNN神经网络的预测结果中相对误差率低于BP神经网络预测结果,其绝对误差率也低于BP神经网络,结论表明,使用GRNN神经网络可以在不需要确定当天气候属于何种天气的前提条件下,实现对于光伏发电量的较准确预测,具有现实应用价值。
The accurate prediction of PV power generation can provide reference for grid smooth operation. Due to the influence of complex weather on the photovoltaic field, it is difficult to accurately predict the quantity of photovoltaic power generation. According to the characteristics of climate change and characteristics of GRNN neural network, A prediction method of the photovohaic power generation by training neural network GRNN is proposed based on historical weather data, simulation results show that, without distinguishing between similar - day circumstances, the prediction method with GRNN neural networks has less relative error rate and lower absolute error rate than the method with BP neural network. The results show that the GRNN neural network can realize the accurate prediction of photovohaic power generation without need to determine the weather conditions of the same day, which has practical application value.
出处
《计算机仿真》
CSCD
北大核心
2016年第12期95-99,304,共6页
Computer Simulation
基金
溪霞水库水力发电电气控制系统(03006005)省部级一般项目:江西省科技支撑计划项目(20151BBE50050)
