Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Olawale, Opeoluwa Wonuola; Gilbert, Ben; Reyna, Janet

doi:10.1016/j.enbuild.2022.111973

Title: Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Journal Article · 22 February 2022 · Energy and Buildings

DOI: https://doi.org/10.1016/j.enbuild.2022.111973 · OSTI ID:1860331

Olawale, Opeoluwa Wonuola ^[1]; Gilbert, Ben ^[2];

^[3]

Colorado School of Mines, Golden, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Colorado School of Mines, Golden, CO (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1860331

Report Number(s):: NREL/JA-5500-80981; MainId:79757; UUID:944b1e66-d024-4f8f-8f9f-7661c34ae213; MainAdminID:64124

Journal Information:: Energy and Buildings, Journal Name: Energy and Buildings Vol. 262; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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