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A systematic approach for data analysis and prediction methods for annual energy profiles: an example for school buildings in Norway
Energy and Buildings ( IF 6.6 ) Pub Date : 2021-06-04 , DOI: 10.1016/j.enbuild.2021.111160
Yiyu Ding , Helge Brattebø , Natasa Nord

Current research on energy supply systems and building energy demand presents positive impacts from the two sides, with potentials of combining top-down and bottom-up modelling. Mostly, the energy demand input has been employed directly from energy utility companies as a package of information, without considering energy use patterns regarding building type. There lacks a bridge between demand profiles on building stock functions and urban energy supply systems. Accordingly, this article proposes a framework that enables the prediction of annual energy profiles, applied to one educational building type on an hourly basis. The work consists of five steps: (1) getting energy information of 40 district heating (DH) supplied schools in Norway, (2, 3) processing data for getting the modified average hourly demand per m2 and holiday breakpoints through a modified Z-Score, (4) energy forecast of DH and electricity load profiles through temperature moving average, correlation, and linear regression analysis, (5) validation of the predicted yearly profiles by three criteria, and further with the cluster methods for DH profiles. The results showed that the suggested methods for annual energy forecast were satisfying. The defined load profiles might represent the current energy demand of the Nordic school and the methods could be transferred to other building types. With energy analysis of typical building types, the proposed method enables the planners to better understand the energy needs for different building functions.



中文翻译:

年度能源概况数据分析和预测方法的系统方法:以挪威学校建筑为例

当前对能源供应系统和建筑能源需求的研究呈现出两者的积极影响,具有自上而下和自下而上建模相结合的潜力。大多数情况下,能源需求输入是直接从能源公用事业公司作为信息包使用的,没有考虑与建筑类型相关的能源使用模式。建筑存量功能的需求概况与城市能源供应系统之间缺乏桥梁。因此,本文提出了一个框架,可以预测年度能源概况,每小时应用于一种教育建筑类型。工作包括五个步骤:(1)40区域供热(DH)在挪威供给学校获得能量信息,(2,3),用于获得每米经修饰的平均每小时需求处理数据2和假期断点,通过修改的 Z 分数,(4) 通过温度移动平均、相关性和线性回归分析对 DH 和电力负荷曲线的能源预测,(5) 通过三个标准验证预测的年度曲线,并进一步与DH 配置文件的聚类方法。结果表明,所提出的年度能源预测方法是令人满意的。定义的负载曲线可能代表北欧学校当前的能源需求,并且这些方法可以转移到其他建筑类型。通过典型建筑类型的能源分析,所提出的方法使规划者能够更好地了解不同建筑功能的能源需求。

更新日期:2021-06-05
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