基于相似日和动量法优化BP神经网络的光伏短期功率预测研究  

Short-term PV Power Prediction Based on BP Neural Network Optimized by Similar Daily and Momentum Method

作　　者：张慧娥 刘大贵[2,3] 朱婷婷 刘光辉 胡衡 肖长江 ZHANG Hui′e;LIU Dagui;ZHU Tingting;LIU Guanghui;HU Heng;XIAO Changjiang(College of Energy Engineering,Xinjiang Institute of Engineering,Urumqi 830047,China;College of Electrical Engineering,Xinjiang University,Urumqi 830047,China;State Grid Xinjiang Power Dispatching Control Center,Urumqi 830001,China;Xinjiang Airport Urumqi Branch Electric Power Operation Management Center,Urumqi 830000,China)

机构地区：[1]新疆工程学院能源工程学院,新疆乌鲁木齐830047 [2]可再生能源发电与并网技术教育部工程研究中心(新疆大学电气工程学院),新疆乌鲁木齐830047 [3]国网新疆电力公司调度中心,新疆乌鲁木齐830001 [4]新疆机场(集团)乌鲁木齐分公司电力运行管理中心,新疆乌鲁木齐830000

出　　处：《智慧电力》2021年第6期46-52,共7页Smart Power

基　　金：新疆自治区自然科学基金资助项目(2020D01B18)。

摘　　要：提出了相似日和动量优化BP神经网络的光伏短期功率预测方法,采用与输出功率强相关的辐照度作为相似变量选取相似日,通过动量法优化并以相似日历史数据和气象信息作为训练样本建立BP神经网络预测模型。以新疆某光伏电站的实际运行数据进行验证分析,结果表明该方法在晴天和非晴天天气环境下能够达到预测精度,验证了所提模型和算法的准确性和有效性。A short-term photovoltaic power prediction method based on BP neural network with similar days and momentum optimization is proposed.The similar days are selected by using the irradiance which is strongly related to the output power as a similar variable.The BP neural network prediction model is established by using the momentum method and the historical data and meteorological information of similar days as training samples.The actual operation data of a photovoltaic power station in Xinjiang are used for verification and analysis.The results show that the method can achieve accurate prediction accuracy in sunny and non sunny weather,and verifies the accuracy and effectiveness of the proposed model and algorithm.

关 键 词：光伏电站功率预测 相似日 BP神经网络 动量BP法 

分 类 号：TM712[电气工程—电力系统及自动化]