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Further development of the change-point model - Differentiating thermal power characteristics for a residential district in a cold climate

机译:进一步发展变化点模型 - 在寒冷气候中区分住宅区的热功率特性

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The building and service sector accounts for nearly 40% of total energy use in Sweden. The existing, historic building stock accounts for large part of this energy use and comprises an important part of the national pursuit to increase energy efficiency. One main problem for decreasing energy use in the existing building stock is the lack of data describing thermal performance characteristics. This paper presents a novel development of the change-point model for predicting the thermal performance of buildings using selected time periods based on time-dependent variations in climate and user behavior. The predicted thermal power characteristics include total specific heat losses (Q(total)), energy use for hot water circulation (HWC) and hot tap water (HTW), and balance temperature. A residential district with 73 historic buildings in Linkoping, Sweden, has been used as the study object.The developed model is shown to be effective and robust for describing building thermal performance. The average R-2 was 0.70 for predictions of specific heat losses. The sensitivity analyses conclude that the selected time steps and months correspond to the highest R-2 value. The average variation width for prediction of the balance temperature is 0.9 degrees C for buildings in the interquartile range based on a three-year comparison of hourly heating power supply data. Moreover, from a property owner perspective, the model is shown to be useful for identifying deviating thermal power characteristics and can easily be used to get an overview of a district. (C) 2020 Elsevier B.V. All rights reserved.
机译:建筑和服务业占瑞典总能源的近40%。现有的历史建筑股票占这次能源使用的很大一部分,包括国家追求能源效率的重要组成部分。降低现有建筑物库存中能源使用的一个主要问题是缺乏描述热性能特征的数据。本文提出了一种新颖的改变点模型的开发,用于使用所选时间段基于气候和用户行为的时间依赖性变化来预测建筑物的热性能。预测的热功率特性包括总特定的热损失(Q(总)),热水循环的能量用途(HWC)和热自来水(HTW)和平衡温度。住宅区拥有瑞典的纳米威斯汀73个历史建筑,已被用作研究对象。开发的模型被证明是有效和坚固的描述,用于描述建筑物热性能。对于特定热损失的预测,平均R-2为0.70。敏感性分析得出结论,所选时间步长和月份对应于最高的R-2值。基于每小时加热电源数据的三年比较,用于预测平衡温度的平均变化宽度为平衡温度的平衡温度为0.9摄氏度。此外,从业主的角度来看,该模型被证明可用于识别偏离的热功率特性,并且很容易用于获得区域的概述。 (c)2020 Elsevier B.v.保留所有权利。

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