SBIR-STTR Award

This Small Business Innovation Research Phase I project will test the feasibility of a signal processing algorithm to separate energy used by individual appliances from the total natural gas and electricity entering a home. Utility ratepayers may benefit from this detailed information by more-effectively identifying and mitigate inefficient appliances and activities. The new generation of smart electric meters currently being deployed for automated reading and time of use pricing are not be able to provide this level of detail. The signal processing algorithm evaluated in this study is innovative in its use of non-parametric statistical measures and specific conductance signatures to accurately distinguish individual appliance loads. Tests will be performed in three residences to compare the performance of the load disaggregation system with that of isolated electrical power measurements from ten or more appliances. The intended outcome of this project is to create an easy-to-use tool that will continually educate consumers to reduce energy consumption. When coupled with adoption of renewable energy sources, improving energy efficiency reduces greenhouse gas emissions. The commercial potential of this technology is high since residential and small business ratepayers could reduce energy costs if they can reliably identify inefficient appliances or activities. If successful, the energy savings from the average household using this technology would pay for the installed cost of the device in less than three years. Providing appliance specific level load information would transform ratepayers' ability to conserve energy and significantly impact resource consumption. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)

This Small Business Innovation Research (SBIR) Phase II project will further develop the IBUCS' Utility Accountant electrical load disaggregation system and conduct performance trials in small commercial settings. Utility ratepayers need granular information to more effectively identify and mitigate inefficient appliances and activities. The new generation of smart electric meters currently being deployed for automated reading and time of use pricing are not be able to provide this level of detail. The signal processing algorithms evaluated during the Phase I study accurately isolate and quantify the power used by individual appliances from the aggregate power signal of the many appliances present on a leg or circuit. In the proposed project, additional enhancements will be made to the load disaggregation algorithm that will significantly improve system accuracy and reliability. Ratepayers will access a secure webpage to view their cost to operate specific appliances in their building or groups of buildings. The granular perspective provided will enable end-users to (1) calculate their own investment return periods for equipment repairs and upgrades, (2) develop data driven best practices for energy conservation, and (3) ensure that cost reductions are maintained over the long term. The broader impact will occur when the technology is deployed throughout the residential and commercial segments as part of a new generation of smart meters. Commercially-available systems that directly monitor multiple specific loads are more expensive by an order of magnitude and inherently more difficult to install and maintain. Providing appliance specific load information has the potential to transform ratepayers' ability to conserve energy. The intended outcome of this Phase II project is to create an easy-to-use tool that will continually educate small business or franchise operators to reduce energy consumption. Nationally, Quick Serve businesses (i.e. fast-food restaurants, gas stations, and minimarts) account for ~10% of commercial buildings but are generally underserved by energy management companies due to their small size. IBUCS will provide a value-added service to the property manager through an energy management company that serves this segment