Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective
Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective
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DOI：
10.1016/j.enpol.2020.111327
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发表时间：
2020-04-01
期刊：
影响因子：
9
通讯作者：
Grueneich, Dian
中科院分区：
文献类型：
--
作者：
Roth, Jonathan;Lim, Benjamin;Grueneich, Dian
建筑物是城市能源消耗的最大来源。为了减少能源使用，城市正在强制要求建筑物进行能源基准测试，这是一个测量建筑物能源性能的过程，目的是识别效率低下的建筑物。在本文中，我们研究了在两个基准测试模型中使用特定城市的公共开放数据源的可行性，并将结果与使用商业建筑能耗调查（CBECS）数据集（能源之星星星的基础）时的相同模型进行了比较。这两个基准模型使用的数据集包含来自10个主要城市的建筑特征和年度能源使用量。为了检验线性和非线性模型之间的性能差异，我们使用随机森林和套索回归。结果表明，使用开放数据的基准模型优于仅基于CBECS数据集的模型。此外，我们的研究结果表明，建筑面积，物业类型，空调面积和用水量是城市收集的最重要的变量。在展示了使用开放数据的好处之后，我们建议对当前的基准测试实践进行两项更改：（1）支持数据驱动基准框架的新指南，该框架依赖于开放数据和透明的建模过程;（2）支持公布基准结果和激励节能的政策。
Buildings are by far the largest source of urban energy consumption. In an effort to reduce energy use, cities are mandating that buildings undergo energy benchmarking-the process of measuring building energy performance in order to identify buildings that are inefficient. In this paper, we examine the feasibility of using city-specific, public open data sources in two benchmarking models and compare the results to the same models when using the Commercial Building Energy Consumption Survey (CBECS) dataset, the basis for Energy Star. The two benchmarking models use datasets containing building characteristics and annual energy use from ten major cities. To examine the difference in performance between linear and non-linear models, we use random forest and lasso regression. Results demonstrate that benchmarking models using open data outperform models based solely on the CBECS dataset. Additionally, our results indicate that building area, property type, conditioned area, and water usage are the most important variables for cities to collect. Having demonstrated the benefits of using open data, we recommend two changes to current benchmarking practices: (1) new guidelines that support a data-driven benchmarking framework relying on open data and a transparent modeling process and (2) supporting policies that publicize benchmarking results and incentivize energy savings.