CAREER: Bridging the Gap between Engineering Simulation and Reality of Home Energy-Efficiency Improvements via Big-Data Analysis

CBET 1652696 PI：Qiu，Yueming该项目旨在将家庭能源效率改善的实证分析转化为准确，可推广和可扩展的过程。该研究计划将（1）开发一个大数据驱动的能效因果影响评估框架，并提供实现节能的可靠统计证据;（2）研究人类环境生态系统中的各种因素（例如，居住者行为）与节能技术相互作用;（3）评估节能基准信息是否可以改变节能的有效性;（4）根据节能的时间来量化对电网、经济激励和环境的影响;以及（5）完善工程节能模拟建模。该教育计划将（1）通过咨询小组吸引一批能源从业人员;（2）通过开放获取工具教育公众提高能源效率;（3）通过跨学科研讨会促进相关领域研究人员之间的交流;以及（4）培养具有跨学科思维和有效技能的未来工程师和科学家。关键的科学贡献预计来自既有大规模的建筑能源数据集，也有新的计算能源效率评估框架，该框架融合了来自各个学科的知识和进步。为了构建有效的基线能源使用，该框架使用控制组，安装前的时间，控制随时间变化的协变量，灵活的固定效应，面板回归。这个评估框架是及时的，现在智能电表正在成为常态。该项目使用来自亚利桑那州凤凰城的综合数据集，其中包括2013年至今48，000名住宅客户的15分钟间隔客户级能源需求数据，确保统计上稳健和代表性的结果。多年的家电饱和度调查的客户层面的能源效率的功能，技术，居住者的行为，建筑属性和人口统计，以及拟议的评估框架，应克服现有的评估研究中的关键缺陷，包括不适当的基线能源建设，选择偏差，遗漏变量偏差。该项目还应揭示能源效率的复杂影响异质性。对于环境和经济效益的评估，技术影响的日内数据将比以往使用平均日数据的研究提供更精确的结果。该项目还将完善能效绩效的相关工程建模技术。

CBET 1652696 PI: Qiu, YuemingThis project aims to transform empirical analysis of home energy-efficiency improvements into an accurate, generalizable, and scalable process. The research program will (1) develop a big data-driven energy-efficiency causal impact evaluation framework and provide reliable statistical evidence of the realized energy savings; (2) examine how various factors in the human-environmental ecosystem (e.g., occupant behaviors) interact with energy-efficient technologies; (3) evaluate whether an energy-saving benchmarking message can alter the effectiveness of energy efficiency; (4) quantify the impact on power grid, economic incentives, and environment based on timing of energy savings; and (5) refine engineering energy savings simulation modeling. The education program will (1) engage a group of energy practitioners through an advisory group; (2) educate the general public on energy efficiency through an open-access tool; (3) facilitate communication among researchers across related fields through an interdisciplinary workshop; and (4) train future engineers and scientists with an interdisciplinary mindset and effective skills.Key scientific contributions are anticipated to stem from both a large-scale building energy dataset as well as a new computational energy-efficiency evaluation framework that incorporates knowledge and advances from various disciplines. To construct valid baseline energy use, the framework uses control group, pre-installation period, control of time-variant covariates, flexible fixed effects, and panel regressions. This evaluation framework is timely now that smart meters are becoming the norm. The project uses a comprehensive dataset from Phoenix metropolitan Arizona that includes 15 min-interval customer-level energy demand data from 2013-present for 48,000 residential customers, ensuring statistically robust and representative results. Multi-year appliance saturation surveys on customer-level energy efficiency features, technologies, occupant behaviors, building attributes, and demographics, together with the proposed evaluation framework, should overcome key shortcomings in existing evaluation studies, including inappropriate baseline energy construction, selection bias, and omitted variable bias. This project also should uncover the complex impact heterogeneity of energy efficiency. For evaluation of environmental and economic benefits, intraday data of technology-specific impacts will offer more precise results than previous studies using average daily data. The project will also refine relevant engineering-modeling techniques of energy efficiency performance.