Insights from Smart Meters: Identifying Specific Actions, Behaviors, and Characteristics That Drive Savings in Behavior-Based Programs

Todd, Annika; Perry, Marc D.; Smith, Brian V.; Sullivan, M.; Cappers, Peter; Goldman, C.

doi:10.2172/1331044

Cited by 1 publication

(1 citation statement)

References 1 publication

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As another segmentation method, we simply order all households by critical peak energy use (defined as peak hour energy use on proxy critical peak days in the pretreatment period) and then split them into two groups at the 80th percentile value [37]. Thus, the high group represents the top 20% of critical peak energy users, and the low group represents the bottom 80%.…”

Section: High and Low Critical Peak Energy Usementioning

confidence: 99%

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Todd-Blick

Spurlock

Jin

et al. 2020

Energy Research & Social Science

View full text Add to dashboard Cite

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. We use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their realworld response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics -such as peak energy use and "structural winningness" (the ability to save money under a DR program without changing energy-use beha-viors) -and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

show abstract

Section: High and Low Critical Peak Energy Usementioning

confidence: 99%