CAJ | 학술논문

光伏电站输出功率对电网调度有很大影响，但受到太阳辐射强度和气象因素的影响，光伏电站输出功率具有随机性和不可控性。为合理利用光伏发电系统，建立一种基于气象预测信息以及BP神经网络的光伏电站输出功率预测模型。通过相关性分析确定影响光伏出力的影响因子，结合历史数据和气象因素进行模型训练和功率预测。文中主要提出一种新的预测模型－双层BP神经网络模型，通过对某光伏电站预测结果与实测值对比，结果表明该方法能有效提高光伏电站输出功率预测精度，对发电计划的制定有较好的参考价值和实用价值。
광복전참수출공솔대전망조도유흔대영향，단수도태양복사강도화기상인소적영향，광복전참수출공솔구유수궤성화불가공성。위합리이용광복발전계통，건립일충기우기상예측신식이급BP신경망락적광복전참수출공솔예측모형。통과상관성분석학정영향광복출력적영향인자，결합역사수거화기상인소진행모형훈련화공솔예측。문중주요제출일충신적예측모형－쌍층BP신경망락모형，통과대모광복전참예측결과여실측치대비，결과표명해방법능유효제고광복전참수출공솔예측정도，대발전계화적제정유교호적삼고개치화실용개치。
Photovoltaic power station output power has a great influence on power grid dispatching , but the photovolta-ic power station output power is random and uncontrollable due to the intensity of solar radiation and meteorological factors.In order to make reasonable use of the photovoltaic power generation system , a photovoltaic power station out-put power model based on weather forecast information and the BP neural network was proposed in the paper .The in-fluential factors of PV station output power were determined through correlation analysis .The model training and power prediction were processed by combination with historical data and meteorological factors .A new forecasting model , the double BP neural network model , was proposed in this paper .The predicted results of a photovoltaic power station were compared with the measured values .The results showed that the method could effectively improve the predictive accuracy of the PV station output power and had good reference values and practical values to power generation plan -ning.