Disaggregated load profiles diagram.

Disaggregated load profiles.

Oak Ridge National Laboratory.

Lead Performer: Oak Ridge National Laboratory – Oak Ridge, TN
-- University of Tennessee – Knoxville, TN
-- Richman Surrey Inc. – Scottsdale, AZ
DOE Funding: $995,000
Project Term: October 1, 2014 - September 30, 2016
Funding Opportunity: Building Energy Efficiency Frontiers and Incubator Technologies (BENEFIT) - 2014 (DE-FOA-0001027)

Project Objective

ORNL, in partnership with Richman Surrey and the University of Tennessee, propose to develop a comprehensive non-intrusive load monitoring system capable of identifying opportunities for energy efficiency within building subsystems. This integrated system consists of: (1) low-cost, non-intrusive power metering to augment existing sensor sources; (2) an integrated power disaggregation fault identification system based on signal unmixing techniques; and (3) a capability to deliver diagnosis information to building managers with impact of fault on energy efficiency for rapid response. This integrated predictive technology will identify equipment degradation and inefficiencies in energy delivery and improve the energy efficiency of the buildings by 15-25% while reducing the cost of deployment by 20-30% compared to the current sparse field diagnostics alternatives. Research indicates that timely FDD could direct maintenance actions and improve energy efficiency by about 15% to 30%, which represents a savings of up to 0.4 quads per year.

Project Impact

Buildings in the United States consumed approximately 41% of energy produced in 2010. This equates to 40 quads of total energy and 12.8 quads of electrical energy for buildings in the U.S. One technology that can contribute to reduced energy consumption is building equipment health monitoring because building equipment degrades over time. Research indicates that equipment issues are quite common in practice.

The small and medium commercial building industry needs a scalable, robust health monitoring platform consisting of sensing, computation, and visualization that is suitable for retrofit applications at a practical installed cost. To increase the energy efficiency of buildings, ORNL is proposing a signal processing and disaggregation approach in which a single-point electric power measurement with intelligent signal unmixing techniques is used to monitor building equipment health.


DOE Technology Manager: Marina Sofos
Principal Investigator: Teja Kuruganti, Oak Ridge National Laboratory

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