电力系统自动化2025，Vol.49Issue(9)：146-156,11.DOI:10.7500/AEPS20240723006
考虑屋顶光伏热效应的短期净负荷预测
Short-term Net Load Forecasting Considering Thermal Effect of Rooftop Photovoltaic
摘要
Abstract
For low-rise industrial buildings with poor thermal insulation performance and large surface area,the shading and insulation effect of their rooftop photovoltaic modules will have a huge impact on the magnitude and fluctuation pattern of the daily load.However,the research on net load forecasting mostly focuses on the single characteristics of load and photovoltaic,with little consideration given to the thermal effects of photovoltaics on load.In response to the above shortcomings,this paper takes an industrial building in Shanghai,China as the research object,constructs a photovoltaic-rooftop integrated heat transfer model through the thermal balance method,calculates the annual thermal effect of rooftop photovoltaic,and verifies the strong correlation between hourly heat transfer and photovoltaic cell temperature with load and photovoltaic output through correlation analysis.Subsequently,in order to more accurately extract the behavior characteristics of the load,the paper conducts cluster analysis on the daily load of each season based on the waveform features of heat transfer.Finally,using heat transfer characteristics as input factors and bi-directional long short-term memory network as forecasting algorithm,a short-term net load forecasting method considering the thermal effect of rooftop photovoltaic is proposed.The net load data of the building in each season is forecasted,modeled,calculated,and error analyzed.Horizontal comparison with long short-term memory network-attention mechanism,long short-term memory network and extreme learning machine shows that the proposed method can significantly improve the accuracy of net load forecasting.关键词
建筑能耗/屋顶光伏/热效应/传热模型/负荷预测Key words
building energy consumption/rooftop photovoltaic/thermal effect/heat transfer model/load forecasting引用本文复制引用
李芬,李雨欣,王亚维,孙改平,刘蓉晖,屈爱芳..考虑屋顶光伏热效应的短期净负荷预测[J].电力系统自动化,2025,49(9):146-156,11.基金项目
国家自然科学基金资助项目(12071298) （12071298）
已申请国家发明专利(申请号:2024109698525). This work is supported by National Natural Science Foundation of China(No.12071298). （申请号:2024109698525）
