Reducing plug-load electricity footprint of residential buildings through low-cost, non-intrusive sub-metering and personalized feedback technology

Lead PI: Patricia Culligan , Dr. Christoph Johannes Meinrenken , , Kathleen McKeown

Unit Affiliation: Columbia Engineering

Unit Affiliation: Research Program on Sustainability Policy and Management (SPM)

September 2016 - December 2022
North America ; United States
Project Type: Research

DESCRIPTION: Essential data from pervasive low-cost submetering with sufficient accuracy for equipment and plug loads is necessary to maximize and verify energy savings, as well as to provide critical information on the state and usage patterns of specific equipment to enable monitoring-based commissioning and facilitate the optimization of fault detection and diagnostics of operational faults along with control strategies and integration with the electric grid. This project will leverage existing nonintrusive submetering in developing a human-in-the-loop approach and investigating occupant feedback strategies to change electricity use by reducing load or shifting usage to non-peak hours.

Appliance-level consumption will be obtained via statistical disaggregation of apartment-level metering in the especially challenging and underserved multifamily residential building sector to eliminate the need for costly and intrusive plug load monitors and reduce electric bills. To target usage feedback to the desired consumption change, the project will utilize natural language processing (NLP) based approaches that automatically generate feedback messages that contain various types of illustrations and text. Statistical analysis will be used on disaggregated appliance-level consumption to determine how much reduction or load-shift in electricity use and in electric bills each type of feedback can achieve, and how this may vary by demographic.

OUTCOMES: Provided unique R&D datasets to stakeholders and enabled grid-interactive efficient buildings.


Department of Energy




Ali Mehmani (Co-Principal Investigator)


Department of Computer Science