Encouraging customers to enrol into smart thermostat demand response program

Last registered on April 02, 2024


Trial Information

General Information

Encouraging customers to enrol into smart thermostat demand response program
Initial registration date
March 28, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
April 02, 2024, 11:11 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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Primary Investigator

The Behaviouralist

Other Primary Investigator(s)

PI Affiliation
University of Southern California

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Countries around the world are transitioning away from fossil fuels to renewable energy sources to meet their Net Zero targets. While beneficial for the environment, this transition brings with it an important challenge: it is not (presently) possible to rapidly increase or decrease the generation of solar, wind and water power to meet existing fluctuations in demand. One solution to this problem is to encourage demand flexibility, which refers to the capability, motivation and willingness of consumers to adapt their energy usage in response to the needs of the grid.

Residential consumers (i.e., households), which are responsible for approximately a fourth of global energy consumption, have a large potential for demand flexibility. One way to achieve this potential is through installation of demand response technologies such as smart thermostats. With a smart thermostat installed, residents can enable their utility provider to temporarily adjust their indoor temperature by a few degrees when the demand for energy is at its highest. When this adjustment is done across hundreds or thousands of households, it can considerably alleviate the stress on the grid.

This study will evaluate the impact of behavioral communication on household adoption of smart thermostats and enrollment in a residential demand response program. We will also assess whether enrollment in the program affects household energy consumption, delivering energy flexibility at times when it is needed.
External Link(s)

Registration Citation

Akesson, Jesper and Robert Metcalfe. 2024. "Encouraging customers to enrol into smart thermostat demand response program." AEA RCT Registry. April 02. https://doi.org/10.1257/rct.13148-1.0
Experimental Details



Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. Smart thermostat take-up and enrollment into a demand response program
2. Energy consumption

Primary Outcomes (explanation)
We define smart thermostat take-up as purchasing a smart thermostat through the utility provider.

We define demand response program enrollment as either purchasing a smart thermostat through the utility provider (and thus automatically enrolled) or joining the program by enrolling an existing smart thermostat.

We define energy consumption as the amount of kWh consumed by households.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomize approximately 140,000 customers into four trial groups (one control and three treatment groups) in partnership with a Canadian utility. Customers in the treatment groups will receive emails and postcards that promote smart thermostats and a demand response program offered by the utility. Each treatment group will receive different communication (text and visuals) that appeals on different motives to install a smart thermostat and enrol into the demand response program. The control group will not receive any communication as part of this trial.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
postal regions
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
2,532 postal regions
Sample size: planned number of observations
141,328 utility customers
Sample size (or number of clusters) by treatment arms
Pure control (N = 30,000), incentive (N = 37,866; 873 unique postcodes), environmental benefits (N = 37,552; 845 unique postcodes), smart thermostat benefits (N = 35,910; 814 unique postcodes).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number