Is it necessary to calibrate existing decision-making results based on real option analysis during the process of climate change? A case study from Xiamen, China

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Wenqiang Li , Xin Jin , Pei Peng , Zaiyi Liao , Min Wu , Huahua Xu , Jiashun Feng
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Abstract

Previous studies have rarely examined how well Representative Concentration Pathways (RCP) scenarios fit with real weather data making climate change related decision-makings. This paper proposes a real option analysis (ROA) decision-making calibration method based on RCP (learning) scenarios uncertainty caused by the gap between the simulated and real weather conditions. Firstly, EnergyPlus is used to predict the energy consumption of a case building based on future climatic conditions, and the learning scenarios uncertainty is quantified. Secondly, a ROA decision-making calibration model (ROACM) is established through optimization to minimize the learning scenarios uncertainty. Finally, the ROACM is applied to a case study of an office building located in Xiamen, China, aiming to calibrate individual and sequential investment decisions for three retrofitting measures (R1: horizontal shading, R2: low-e windows, R3: improving COP of chiller). The accuracy of ROACM is assessed through a sensitivity analysis. The results indicate that the learning scenarios uncertainty leads to annual energy consumption differences of approximately 8,451 kW h to 17,545 kWh in the case study. After calibration, the optimal timing for both individual and sequential investment has advanced by at least 4 years, significantly boosting returns. The optimal individual investment R3 generated an additional return of up to $52,744, while the sequential investment R1+R13 (shading first, and improving COP of chiller later), increased by $175,017. The proposed ROACM demonstrated high performance during model validation in a case study, and could also aid governments or investors in ROA-based mitigation strategies analysis and increasing the reliability of the results.
在气候变化过程中,是否有必要对现有的基于实物期权分析的决策结果进行校正?以中国厦门为例
以前的研究很少检查代表性浓度路径(RCP)情景在制定气候变化相关决策时与真实天气数据的匹配程度。本文提出了一种基于RCP(学习)场景不确定性的实物期权分析(ROA)决策校准方法。首先,利用EnergyPlus基于未来气候条件对案例建筑的能耗进行预测,量化学习场景的不确定性;其次,通过优化建立ROA决策校准模型(ROACM),最小化学习场景的不确定性;最后,将ROACM应用于中国厦门的一栋办公楼的案例研究,旨在为三种改造措施(R1:水平遮阳,R2:低能耗窗户,R3:提高制冷机的COP)校准单个和连续的投资决策。通过敏感性分析评估ROACM的准确性。结果表明,在案例研究中,学习场景的不确定性导致年能耗差异约为8,451 kW∙h至17,545 kW∙h。校正后,个人和顺序投资的最佳时机都提前了至少4年,显著提高了回报。最佳个人投资R3产生了高达52,744美元的额外回报,而顺序投资R1+R13(首先遮阳,然后提高制冷机的COP)增加了175,017美元。拟议的ROACM在案例研究中的模型验证过程中表现出高性能,还可以帮助政府或投资者进行基于ROACM的缓解战略分析,并提高结果的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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