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

PI：邱跃明本项目旨在将家庭能效改进的实证分析转化为一个准确、可推广、可扩展的过程。研究项目将(1)建立大数据驱动的能效因果影响评估框架，为实现的节能提供可靠的统计证据；(2)研究人类-环境生态系统中的各种因素（如居住者行为）如何与节能技术相互作用；(3)评估节能标杆信息是否能改变能效的有效性；(4)基于节能时机量化对电网、经济激励和环境的影响；(5)细化工程节能仿真建模。该教育计划将(1)通过一个咨询小组吸引一组能源从业人员；(2)透过一个开放获取的工具，教育公众认识能源效益；(3)通过跨学科研讨会促进相关领域研究人员之间的交流；(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。预计主要的科学贡献将来自大规模的建筑能源数据集，以及一个新的计算能源效率评估框架，该框架结合了不同学科的知识和进步。为了构建有效的基线能源使用，该框架使用了控制组、预安装期、时变协变量控制、灵活固定效应和面板回归。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个住宅客户的15分钟间隔客户级能源需求数据，确保统计上稳健且具有代表性的结果。对客户层面的能效特征、技术、使用者行为、建筑属性和人口统计数据进行多年的家电饱和度调查，以及拟议的评估框架，应克服现有评估研究中的主要缺点，包括不适当的基线能源结构、选择偏差和遗漏的变量偏差。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，技术特定影响的每日数据将比以前使用平均每日数据的研究提供更精确的结果。该项目还将完善能源效率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.