Predicting Building Energy Efficiency Using New York City Benchmarking Data
Predicting Building Energy Efficiency Using New York City Benchmarking Data
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发表时间：
2012
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通讯作者：
Constantine E. Kontokosta
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作者：
Constantine E. Kontokosta
降低改造后性能和节能方面的风险和不确定性是为商业建筑改造打开传统资本市场的关键组成部分。这反过来又是扩大可持续的真实的房地产市场和改造方案的必要步骤。虽然有一系列因素影响实际的节能，但我们对建筑特征如何影响能源性能的理解仍然存在差距。使用一个独特的数据库的能源基准数据产生的纽约市的地方法84，访问和分析的第一次由作者，本文探讨了能源性能的范围内的建筑特征，包括结构，机械，位置，和占用变量。本文中使用的前所未有的数据允许分析超过10，000个大型（超过50，000平方英尺）的商业建筑，是第一个重要的，强大的非自愿能源绩效报告样本。利用稳健回归技术，本研究提出了一个建筑节能的预测模型。这项工作是特别相关的能源基准，改造融资计划和商业租赁结构，旨在克服分裂激励障碍的新兴政策举措。
The reduction of risk and uncertainty with respect to post-retrofit performance and energy savings is a critical component to unlocking traditional capital markets for commercial building retrofits. This, in turn, is a necessary step in scaling up sustainable real estate markets and retrofit programs. While a range of factors influence actual energy savings, there remains a gap in our understanding of how building characteristics affect energy performance across a broad spectrum of variables. Using a unique database of energy benchmarking data resulting from New York City’s Local Law 84, accessed and analyzed for the first time by the author, this paper examines energy performance across a range of building characteristics, including structural, mechanical, locational, and occupancy variables. The unprecedented data used in this paper allows for the analysis of over 10,000 large (over 50,000 square feet) commercial buildings and is the first significant, robust sample of non-voluntary energy performance reporting. Using robust regression techniques, this research then presents a predictive model of building energy efficiency. This work is particularly relevant to emerging policy initiatives relating to energy benchmarking, retrofit financing programs, and commercial lease structures designed to overcome split incentive barriers.