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

Arizona State University, Scottsdale AZ

Investigators

Abstract

CBET 1652696 PI: Qiu, Yueming This 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.