CAJ | 학술논문

为提高非理想天气条件下的光伏功率预测精度,提出基于灰色系统校正?小波神经网络(wavelet neural network, WNN)的预测方法.首先以基于相似日算法的WNN进行逐时功率预测,并进行累加获得日累加功率.根据光伏出力历史数据,确定各广义天气类型的平均偏差比,并以平均偏差比进行平滑处理后的相邻日功率建立离散灰色系统模型(discrete gray model,DGM),进行日总功率预测并获得及其判断区间.最后以日总功率值判断区间为标准对累加功率值进行校正,得到校正后的各时段的预测值.算例结果验证了所提方法的有效性.
위제고비이상천기조건하적광복공솔예측정도,제출기우회색계통교정?소파신경망락(wavelet neural network, WNN)적예측방법.수선이기우상사일산법적WNN진행축시공솔예측,병진행루가획득일루가공솔.근거광복출력역사수거,학정각엄의천기류형적평균편차비,병이평균편차비진행평활처리후적상린일공솔건립리산회색계통모형(discrete gray model,DGM),진행일총공솔예측병획득급기판단구간.최후이일총공솔치판단구간위표준대루가공솔치진행교정,득도교정후적각시단적예측치.산례결과험증료소제방법적유효성.
To improve prediction accuracy of photovoltaic power under non-ideal weather conditions, a wavelet neural network (WNN) prediction model of PV output is proposed based on correction of gray system model. Firstly, hourly power is predicted with WNN based on similar day algorithm, daily accumulative power is obtained by summing the hourly powers. Then, average deviation ratio of each generalized type of weathers is determined according to historical photovoltaic output data. Adjacent daily power is smoothed with average deviation ratio used to build discrete gray model (DGM), to obtain daily total power and its judgment interval. Finally, daily accumulative power is corrected with judgment interval as reference to obtain post-correction hourly power. Effectiveness of the method is validated with a practical example.