Building stock energy modeling considering building system composition and long-term change for climate change mitigation of commercial building stocks
Building stock energy modeling considering building system composition and long-term change for climate change mitigation of commercial building stocks
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考虑建筑系统组成和商业建筑群气候变化缓解的长期变化的建筑群能源建模
DOI：
10.1016/j.apenergy.2021.117907
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发表时间：
2022
期刊：
影响因子：
11.2
通讯作者：
Shimoda Y.
中科院分区：
文献类型：
--
作者：
Yamaguchi Y;Kim B;Kitamura T;Akizawa K;Chen H;Shimoda Y.
显著提高建筑物的能源效率对于减缓气候变化至关重要。采用参考建筑物模型的建筑物存量能源模型（BSEMs）对缓解分析很有用。然而，大多数现有的BSEMs开发的商业建筑股票集中在有限的建筑系统和节能措施，技术部署建议基于简单的老式驱动的场景。这些方法是不够的，特别是对于改善建筑物绝缘性能可能产生适度影响的地区。本研究的目的是建立一个BSEM框架，以克服这个问题，并通过其应用于日本商业建筑股票验证框架。该框架开发的统计模型，估计系统的选择概率的替代品，并利用它们来分解建筑库存。为每个库存段开发了参考建筑模型。结果表明，这种方法有利于使用多种技术方案，考虑到各种因素影响的技术部署，它也有助于估计建筑存量的基线发展。此外，所开发的模型很好地代表了能源使用强度的分布，并估计了建筑存量的总能源消耗具有合理的准确性。据估计，到2030年，基线开发将使二氧化碳排放量从2013年减少18%。节能措施有助避免因增加暖气、通风、空调及热水系统的热源而增加电力需求。由于BSEM开发不是信息密集型的，因此该框架有助于扩展BSEM的应用范围。
A significant improvement in the building stock energy efficiency is imperative to mitigate climate change. Building stock energy models (BSEMs) that employ reference building models are useful for mitigation analysis. However, most existing BSEMs developed for commercial building stock focus on limited building systems and energy conservation measures, and technology deployments are suggested based on simple vintage-driven scenarios. These approaches are insufficient, particularly for regions where improvements to building insulation performances can have a modest impact. This study aims to establish a BSEM framework to overcome this issue and to validate the framework via its application to the Japanese commercial building stock. The framework develops statistical models for estimating the selection probabilities of system alternatives and utilizes them to disaggregate building stocks. A reference building model is developed for each stock segment. The results show that this approach facilitates the use of multiple technological options considering various factors that affect technological deployments, and it also helps estimate the baseline development of building stocks. Furthermore, the developed model well represents the observed distributions in energy use intensity and estimates the aggregated energy consumption of building stocks with a reasonable accuracy. The baseline development was estimated to reduce the CO2emission by 18% by 2030 from 2013. Efficiency measures can help avoid the increase in electricity demand caused by electrifying the heat source of heating, ventilating, and air-conditioning and water heating systems. The framework could help extend the scope of BSEM application because BSEM development is not information intensive.
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