| NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
| Recipient: |
|
| Initial Amendment Date: | September 15, 2017 |
| Latest Amendment Date: | September 15, 2017 |
| Award Number: | 1757329 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Bruce Hamilton
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
| Start Date: | July 1, 2017 |
| End Date: | January 31, 2023 (Estimated) |
| Total Intended Award Amount: | $502,000.00 |
| Total Awarded Amount to Date: | $502,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
3112 LEE BUILDING COLLEGE PARK MD US 20742-5100 (301)405-6269 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
MD US 20742-5141 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | EnvS-Environmtl Sustainability |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Studies on the impact of energy-efficient technologies have shown that the actual energy savings are lower than theoretical predictions from engineering models. Existing energy-efficiency evaluation frameworks and traditional statistical analysis are not enough to identify the causal impact of energy efficiency in reality because households mostly self-select into energy-efficiency installations and the observed changes in energy consumption after the installations could be partially due to certain factors that are generally time-variant and unobservable to the statistician such as occupant behaviors. This project aims to transform empirical analysis of energy-efficiency improvements into an accurate, generalizable, and scalable process, using high frequency smart meter electricty demand data.
The research program has (1) developed a big data–driven energy-efficiency causal impact evaluation framework and provided reliable statistical evidence of the realized energy savings of various technologies including energy efficient air conditioners, heat pumps, solar panels, and electric vehicles; (2) examined how various factors in the human-environmental ecosystem (e.g., occupant behaviors) interact with energy-efficient technologies including income, race and ethnicity, external environmental factors, government incentives, and electricity pricing; (3) evaluated consumers’ attitudes towards energy efficiency and other low-carbon technologies via consumer survey; (4) quantified the impact on power grid, economic incentives, and environment based on timing of energy savings from various low-carbon technologies; and (5) refined engineering energy savings simulation modeling via comparison between engineering-predicted savings versus actual savings. Overall, we have found that there are heterogeneous changes in electricity consumption behaviors due to energy efficiency and other low-carbon technology adoption, and not all types of behaviors are consistent with those being predicted by engineering and rational economics models. These different behaviors can change the environmental impacts, and private and public benefits of these technologies. Factors such as income, race and ethnicity, external environmental factors, government incentives, and electricity pricing impact these behaviors. Building simulations models and policy assessment framework should consider such heterogeneous behaviors and incorporate such behavior changes in their models.
The education program has (1) engaged a group of energy practitioners through an advisory group; (2) educated the general public on energy efficiency through public-facing workshops and seminars; (3) facilitated communication among researchers across related fields through an interdisciplinary workshop; and (4) trained future engineers and scientists with an interdisciplinary mindset and effective skills.
Last Modified: 05/06/2023
Modified by: Yueming Lucy Qiu
Please report errors in award information by writing to: awardsearch@nsf.gov.
