基于庞大算例变量提取的办公建筑能耗预测方法及应用  

Energy Consumption Prediction Method of Office Building Based on the Variables Extraction From a Large-scale Simulation Database and a Case Study

作　　者：姬颖 连会会 陈永保[3] 谢静超[1] 刘加平[1] JI Ying;LIAN Huihui;CHEN Yongbao;XIE Jingchao;LIU Jiaping(Faculty of Architecture,Civil and Transportation Engineering,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology,Beijing University of Technology,Beijing 100124,China;School of Energy and Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]北京工业大学城建学部,北京100124 [2]北京工业大学绿色建筑环境与节能技术北京市重点实验室,北京100124 [3]上海理工大学能源与动力工程学院,上海200093

出　　处：《北京工业大学学报》2023年第3期386-394,共9页Journal of Beijing University of Technology

基　　金：国家自然科学基金青年基金资助项目(51908006);“十三五”国家重点研发计划资助项目(2018YFC0705900)。

摘　　要：模拟法应用专业软件,可准确计算动态能耗,但输入参数烦琐且建筑几何模型确定后往往无法更改;数据挖掘法计算速度快,适用条件多样,但是需要长时间历史数据进行训练,效果受样本数据限制.针对以上问题,提出一种基于庞大算例变量提取的办公建筑能耗预测模型,利用EnergyPlus建立批量典型建筑模型,调整建筑参数生成百万条数据作为训练数据集;采用LightGBM算法,筛选影响负荷的特征因素,构建负荷预测模型;结合EnergyPlus中空调设备能耗计算模型,应用python编译实现能耗预测,并在北京某办公建筑中进行应用和验证.结果表明,筛选的24维特征变量,可保证模型预测准确度在90%以上,逐日能耗的预测平均相对误差为8.27%.应用标准年气象参数计算全年建筑能耗,逐月平均相对误差为10.37%,建筑实际能耗指标为35.20 kW·h/(m2·a),预测能耗指标为36.25 kW·h/(m2·a),相对误差为2.98%.Using professional software, the simulation method can accurately calculate the dynamic energy consumption, however, the input parameters are cumbersome and often cannot be changed after the building geometric model is determined. Data mining method is fast and can be applied in various conditions, however, it needs long-time historical training data and is greatly affected by data quality. Based on the above characteristics, an energy consumption prediction model of office buildings was proposed based on the variables extraction from large simulation examples in this paper. EnergyPlus was used to build bulk models of typical office buildings and adjust input parameters to generate a database of millions of data. LightGBM algorithm was used to screen the characteristic factors affecting the load and construct the load forecasting model. Results show that the selected 24 dimensional characteristic variables can ensure that the prediction accuracy of the model is more than 90%. Combined with the energy consumption calculation model of air-conditioning equipment in EnergyPlus, the energy consumption prediction was achieved by python compilation. An office building in Beijing was selected as a research case. The average relative error of daily energy consumption of the measurement period was 8.27%. Then, typical annual weather data was used to calculate annual building energy consumption and a monthly average relative error of 10.37% was obtained. The predicted energy use intensity was 36.25 kW·h/(m~2·a), while the real value was 35.20 kW·h/(m~2·a), with a relative error of 2.98%.

关 键 词：办公建筑 能耗预测 模拟数据库 特征变量 ENERGYPLUS LightGBM 

分 类 号：TU831[建筑科学—供热、供燃气、通风及空调工程] TP181[自动化与计算机技术—控制理论与控制工程]