基于SVR残差修正的光伏发电功率预测模型

Prediction model of photovoltaic power generation based on SVR residual correction

近年来,大规模光伏并网对区域电网的安全稳定运行造成了严重影响。光伏功率超短期预测可为区域电力调度提供必要的数据支撑,促进新能源消纳,但光伏自身的波动特性使光伏功率预测的精度难以提高。因此,文中提出考虑功率修正、基于差分自回归移动平均(ARIMA)和支持向量回归(SVR)的光伏发电功率预测模型。首先,以光伏电站现场采集的功率时间序列建立ARIMA模型,对日发电功率进行初步预测;其次,利用前一个气象相似日的预测残差数据建立SVR模型,对预测日的ARIMA残差进行预测;最后,对初步预测结果进行修正。利用现场实测数据建立典型日的光伏发电预测模型,测试结果表明在残差修正后,预测精度明显提升。

In recent years,large-scale photovoltaic( PV) grid connection has seriously affected the safe and stable operation of regional power grid. Ultra-short-term prediction of PV power can provide necessary data support for regional power dispatching and promote the realization of new energy consumption goal. However,the fluctuation characteristics of PV power make it difficult to improve the accuracy of power prediction. Therefore,PV power prediction model based on autoregressive integrated moving average( ARIMA) and support vector regression( SVR) considering power correction is proposed. Firstly,the ARIMA model is established using time series power data collected by PV power monitoring system,and preliminary prediction results can be obtained. Secondly,the prediction residuals of the previous meteorological similar day are used to establish SVR model to obtain the residuals of the prediction day. Finally,the preliminary prediction results are revised by prediction residuals. The typical PV power prediction models of different weather conditions are established by using the measured data. Test results show that the prediction accuracy is obviously improved after the residual error correction.