DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis
DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis
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DOI：
10.1016/j.enbuild.2017.12.040
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发表时间：
2018-03
影响因子：
6.7
通讯作者：
Zheng Yang;J. Roth;Rishee K. Jain
中科院分区：
文献类型：
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作者：
Zheng Yang;J. Roth;Rishee K. Jain
随着世界城市化进程的加快，解决能源密集型城市建筑环境问题变得越来越重要。美国和世界各地的城市都将能源基准作为一种了解其建筑存量的相对能源效率和确定减少能源使用的潜在机会的手段。标杆管理利用建筑物的特点和能源使用数据，根据性能基准来衡量建筑物的能源消耗，并得出一个能源效率水平。美国20多个城市和世界上许多其他城市已经通过法律，要求收集和披露能源使用数据，以便制定基准并确定潜在的节能机会。然而，市政当局正在努力将这些数据转化为可操作的见解，并确定哪些建筑物是能效干预的主要候选者。尽管在建筑能源性能基准方面存在大量工作，但以前的工作在利用这些新兴数据流并在城市规模上进行分析的能力方面受到限制。此外，以前的方法主要基于黑箱模型，这限制了结果的可解释性，从而阻碍了决策者在其政策设计和决策过程中使用这种模型的能力。本文提出了一种基于递归划分和随机前沿分析的数据驱动城市能源基准分析方法——DUE-B。为了测试DUE-B，我们使用纽约市10,000多座建筑物的真实能源和建筑数据来评估其性能，并使用Kendall tau-b相关系数将结果与其他常见基准模型进行比较。结果表明，在识别高效和低效建筑子集方面，DUE-B比传统的基准测试方法更加稳健。此外，我们强调了市政官员和其他政策制定者如何利用DUE-B的结果来针对低效建筑进行能效干预、激励和计划。具体来说，我们指出了市政当局如何利用DUE-B来针对最低效的建筑进行补贴的现场能源审计，并针对效率较低的建筑进行低资本密集型的能源效率策略，如激励和教育计划。最后，像DUE-B这样更强大的基准方法有可能提高城市能源效率项目的效率，并帮助城市向更可持续的能源未来过渡。
With the world rapidly urbanizing, addressing the energy intensive urban built environment is becoming increasingly important. Cities across the United States and the world are turning to energy benchmarking as a means of understanding the relative energy efficiency of their building stock and identifying potential opportunities to reduce energy usage. Benchmarking utilizes building characteristics and energy use data to measure a building’s energy consumption against a performance baseline and derive a level of energy efficiency. Over twenty cities in the United States and many others across the world have passed laws mandating the collection and disclosure of energy use data to enable benchmarking and pinpoint potential energy saving opportunities. However, municipalities are struggling to convert this data into actionable insights and identify which buildings are prime candidates for energy efficiency interventions. Although an extensive body of work exists on benchmarking building energy performance, previous works are limited in their ability to leverage such emerging data streams and conduct analysis at the city scale. Moreover, previous methods are largely based on black-box models that limit the interpretability of results and in turn hinder the ability of policy-makers to employ such models in their policy design and decision-making processes. In this paper, we propose DUE-B, a new Data-driven Urban Energy Benchmarking methodology based on recursive partitioning and stochastic frontier analysis. To test DUE-B, we evaluate its performance using real energy and building data from over 10,000 buildings in New York City, and we compare the results to other common benchmarking models using the Kendall tau-b correlation coefficient. Results indicate that DUE-B is more robust than conventional benchmarking methods in respect to identifying subsets of efficient and inefficient buildings. Furthermore, we highlight how results from DUE-B can be utilized by municipal officials and other policy-makers to target inefficient buildings for energy efficiency interventions, incentives, and programs. Specifically, we indicate how DUE-B can be utilized by municipalities to target the most inefficient buildings for subsidized onsite energy audits and less inefficient buildings for less capital-intensive energy efficiency strategies such as incentives and educational programs. In the end, more robust benchmarking methods like DUE-B have the potential to enhance the efficacy of municipal energy efficiency programs and help transition cities to a more sustainable energy future.