CAJ | 학술논문

当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题.基于本地5 kW小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用 BP 以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型.实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度.其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强.
당전재광복전참출력단기예측방면교다적채용BP혹자우화적BP신경망락산법,존재채용적우화산법단일、결핍다충우화산법비교선우、예측오차대적문제.기우본지5 kW소형분포식광복전참,종합고필영향광복출력적태양광복사강도、배경온도、풍속기상상관인소화광복전참역사발전수거,분별채용 BP 이급유전산법화입자군산법우화적BP신경망락산법—GA-BP화POS-BP구건료청천、다운、음우삼충천기조건하광복출력단기예측모형.실측결과표명,삼충신경망락산법예측모형재삼충불동천기조건하균체도료일정적예측정도.기중GA-BP、POS-BP상비전통적BP예측모형강저료예측오차,차POS산법상비GA산법대우BP신경망락예측모형적우화효과경호,진일보강저료예측오차,괄용성경강.
In the current PV output short-term forecast, BP or optimization BP neural network algorithm is used commonly, which has problems of single optimization algorithm, the lack of a variety of optimization algorithms for comparison and selection, and big forecast error. Therefore, based on local 5 kW small-scale distributed PV power station, considering the related factors that influence PV output such as solar radiation intensity, environmental temperature, wind speed and historical generation data of photovoltaic power station, this paper uses BP, GA-BP and POS-BP neural network algorithm respectively to construct short-term prediction model of PV output in sunny, cloudy and rainy weather conditions. Test results show that three kinds of neural network prediction models all reach certain prediction accuracy under three different weather conditions, among which GA-BP and POS-BP prediction models reduce the prediction errors compared to the traditional BP model, and POS algorithm has a better optimization effect on BP neural network prediction model and a stronger applicability compared to GA algorithm, and further reduces the prediction errors.