Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Opeoluwa Olawale, Ben Gilbert, Janet Reyna

Research output: Contribution to journalArticlepeer-review

8 Scopus Citations


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.

Original languageAmerican English
Article number111973
Number of pages15
JournalEnergy and Buildings
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

NREL Publication Number

  • NREL/JA-5500-80981


  • Demand flexibility
  • Extensive and intensive behavior
  • Machine learning models
  • Residential buildings
  • Sociodemographic predictors


Dive into the research topics of 'Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors'. Together they form a unique fingerprint.

Cite this