基于模型-数据联合的光伏-光热系统储能量预测  

Energy Storage Prediction of Photovoltaic-Concentrating Solar Power System Based on Model and Data

作　　者：田亮[1] 王冠杰 TIAN Liang;WANG Guanjie(Department of Automation,North China Electric Power University,Baoding 071003,Hebei Province,China)

机构地区：[1]华北电力大学自动化系,河北保定071003

出　　处：《动力工程学报》2024年第6期911-918,共8页Journal of Chinese Society of Power Engineering

基　　金：国家重点研发计划资助项目(2022YFB4100400)。

摘　　要：提出一种模型-数据联合预测方法,通过机理分析建立对象动态模型,并引入未来太阳辐射强度、用户负荷预测数据进行即时模型预测,通过注意力机制改进的卷积-长短时记忆混合神经网络建立数据预测模型,并引入历史数据进行滚动数据预测,然后利用卡尔曼滤波器对2种预测模型的输出结果进行融合,实现储能量的联合预测。结果表明:联合预测兼具2种方法的优势,能很好地解决储能量预测误差随时间累积的问题,并能够及时表征气象因素突变和系统运行方式改变时储能量的变化情况,在各种天气状况下均具有良好的预测精度。A model-data joint prediction method was proposed.The object dynamic model was established through mechanism analysis,and the future solar radiation intensity and user load prediction data were introduced for immediate model prediction.The data prediction model was established through the convolution-short and long time memory hybrid neural network improved by attention mechanism,and the historical data was introduced for rolling data prediction.Then,Kalman filter was used to combine the output of the two prediction models to realize the joint prediction of energy storage.Results show that the combined prediction has the advantages of both methods,which can solve the problem of accumulated energy storage prediction errors over time and timely characterize the changes of energy storage when meteorological factors suddenly change and system operation mode changes.The proposed method has good prediction accuracy under various weather conditions.

关 键 词：光伏-光热综合能源系统 储能量 预测 卡尔曼滤波 卷积-长短时记忆混合神经网络 

分 类 号：TM615[电气工程—电力系统及自动化]