A data-driven predictive model of city-scale energy use in buildings
A data-driven predictive model of city-scale energy use in buildings
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DOI：
10.1016/j.apenergy.2017.04.005
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发表时间：
2017-07-01
期刊：
影响因子：
11.2
通讯作者：
Tull, Christopher
中科院分区：
文献类型：
--
作者：
Kontokosta, Constantine E.;Tull, Christopher
美国的许多城市已经转向建筑能源披露（或基准）法律，以鼓励能源效率市场的透明度，并支持可持续性和碳减排计划。除了直接的同行比较外，研究人员和政策制定者还将根据这些法律公布的基准数据用作研究大型建筑物能源使用的分布和决定因素的工具。然而，这些政策只覆盖了特定城市中的一小部分建筑物，因此只捕获了城市规模的一小部分能源使用。为了克服这一限制，我们开发了一个预测模型的能源使用在建筑，地区和城市规模使用训练数据从能源披露政策和预测从广泛可用的财产和分区信息。我们使用统计模型来预测纽约市110万栋建筑物的能源使用情况，这些建筑物使用的物理、空间和能源使用属性来自每年需要报告能源使用数据的23，000栋建筑物。线性回归（OLS）、随机森林和支持向量回归（SVM）算法适用于城市的能源基准数据，然后用于预测城市中每个物业的电力和天然气使用量。使用2014日历年的实际消费数据，在建筑物层面和邮政编码层面评估和验证了模型的准确性。我们发现OLS模型在推广到整个城市时表现最好，并且SVM在LL 84样本内预测能源使用的平均绝对误差最低。我们预测的办公楼电能使用强度中位数为71.2 kbtu/sf，住宅楼为31.2 kbtu/sf，平均绝对对数准确率为0.17。建筑物的年龄被认为是能源使用的一个重要预测因素，较新的建筑物（特别是1991年以后建造的建筑物）的消耗水平高于1930年以前建造的建筑物。我们还发现办公楼和零售大楼的电力消耗更高，尽管天然气的情况相反。一般来说，较大的建筑物每平方英尺使用较少的能源，而楼层较多的高层建筑，控制建筑面积，每平方英尺使用更多的能源。附属建筑物-那些与相邻建筑物和共享党墙-被发现有较低的天然气使用强度。结果表明，电力消费可以可靠地预测使用的实际数据从一个相对较小的建筑物的子集，而天然气的使用提出了一个更复杂的问题，鉴于消费和基础设施的可用性的双峰分布。(C)2017爱思唯尔有限公司版权所有
Many cities across the United States have turned to building energy disclosure (or benchmarldng) laws to encourage transparency in energy efficiency markets and to support sustainability and carbon reduction plans. In addition to direct peer-to-peer comparisons, the benchmarking data published under these laws have been used as a tool by researchers and policy-makers to study the distribution and determinants of energy use in large buildings. However, these policies only cover a small subset of the building stock in a given city, and thus capture only a fraction of energy use at the urban scale. To overcome this limitation, we develop a predictive model of energy use at the building, district, and city scales using training data from energy disclosure policies and predictors from widely-available property and zoning information. We use statistical models to predict the energy use of 1.1 million buildings in New York City using the physical, spatial, and energy use attributes of a subset derived from 23,000 buildings required to report energy use data each year. Linear regression (OLS), random forest, and support vector regression (SVM) algorithms are fit to the city's energy benchmarking data and then used to predict electricity and natural gas use for every property in the city. Model accuracy is assessed and validated at the building level and zip code level using actual consumption data from calendar year 2014. We find the OLS model performs best when generalizing to the City as a whole, and SVM results in the lowest mean absolute error for predicting energy use within the LL84 sample. Our median predicted electric energy use intensity for office buildings is 71.2 kbtuisf and for residential buildings is 31.2 kbtu/sf with mean absolute log accuracy ratio of 0.17. Building age is found to be a significant predictor of energy use, with newer buildings (particularly those built since 1991) found to have higher consumption levels than those constructed before 1930. We also find higher electric consumption in office and retail buildings, although the sign is reversed for natural gas. In general, larger buildings use less energy per square foot, while taller buildings with more stories, controlling for floor area, use more energy per square foot. Attached buildings- those with adjacent buildings and a shared party wall- are found to have lower natural gas use intensity. The results demonstrate that electricity consumption can be reliably predicted using actual data from a relatively small subset of buildings, while natural gas use presents a more complicated problem given the bimodal distribution of consumption and infrastructure availability. (C) 2017 Elsevier Ltd. All rights reserved.