Risk analysis of energy efficiency investments in buildings using the Monte Carlo method

Risk analysis of energy efficiency investments in buildings using the Monte Carlo method
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使用蒙特卡罗方法对建筑物能效投资进行风险分析

DOI:
10.1080/19401493.2018.1523949
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发表时间:
2018
影响因子:
2.5
通讯作者:
Togashi Eisuke
Togashi Eisuke
中科院分区:
工程技术4区
文献类型:
--
作者:
森山大輝;大西康伸;藤田真衣;藤岡泰寛・磯崎透子・大原一興;Togashi Eisuke

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证明能源效率投资的经济合理性是减少建筑物能源消耗的必要步骤。一般来说,金融工具是根据投资回报和风险来评估的。然而,许多以前的研究建筑节能投资是基于确定性的情况下,并没有评估这些投资的风险水平。因此,在本研究中,我们明确了节能投资所涉及的风险,通过计算的概率分布的能源减少和评估的结果,使用金融工程方法。我们首先开发了一个随机模型的各种条件,影响建筑物的能源消耗。这些条件包括天气过程、办公室工作人员行为、租户特征和租户更换。接下来,我们构建了一个建筑物的能源消耗的预测模型,我们使用我们的随机模型来创建这个预测模型的边界条件。通过使用蒙特卡罗方法反复进行能耗预测,我们可以获得建筑能耗的概率分布。最后,给出这个概率分布,我们使用金融工程方法评估能源效率投资。基于折现现金流分布,计算每项能效投资的风险溢价,并基于每项能效投资的内部收益率的方差和协方差矩阵,求出最优投资比例。
Demonstrating the economic rationality of investments in energy efficiency is a necessary step in reducing the energy consumption of buildings. Generally, financial instruments are evaluated according to both the return on investment and the risk. However, many previous studies of energy efficiency investments in buildings are based on deterministic scenarios and do not evaluate the risk levels of these investments. Therefore, in this study, we clarify the risk involved in an energy-saving investment by calculating the probability distribution of the energy reduction and evaluating the result using financial engineering methods. We first develop a stochastic model of various conditions that affect the energy consumption of a building. These conditions include weather processes, office worker behavior, tenant characteristics, and tenant replacements. Next, we construct a prediction model of a building's energy consumption, and we use our stochastic model to create the boundary conditions of this prediction model. By repeatedly performing energy consumption predictions using the Monte Carlo method, we can obtain the probability distribution for building energy consumption. Finally, given this probability distribution, we evaluate energy efficiency investments using financial engineering methods. Based on the discounted cash flow distribution, we calculate the risk premium of each energy efficiency investment, and, based on the variance and covariance matrix of the internal rate of return of each energy efficiency investment, we find the optimal investment ratio.
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