A team led by UC-Berkeley researchers will leverage previously developed PowerBlade technology to create a low-cost, wireless plug-load power meter.
January 2, 2018
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Lead Performer: University of California-Berkeley – Berkeley, CA
Partners:
-- Lawrence Berkeley National Laboratory – Berkeley, CA
-- National Renewable Energy Laboratory – Golden, CO
-- CubeWorks – Ann Arbor, MI
-- University of Michigan – Ann Arbor, MI
DOE Total Funding: $2,250,000
Cost Share: $250,000
Project Term: January 1, 2018 – December 31, 2020
Funding Type: Buildings Energy Efficiency Frontiers & Innovation Technologies (BENEFIT) – 2017 (DE-FOA-0001632)
Project Objective
The consumption of miscellaneous energy loads (MELs) is expected to increase significantly by 2030 due to a combination of greater efficiency of loads within a building’s core function and the overall growth of MELs usage. The challenge in developing strategies to reduce consumption of MELs is because they are comprised of a wide array of devices. Furthermore, a significant portion of consumption is attributed to unknown devices not defined in Energy Information Administration (EIA) surveys or analyses.
This project will help address the challenge of identifying loads within the “long tail” of consumption which consists of devices less than 50 W that make up 75% of MELs devices and accounts for approximately 48% of their energy use. The team led by the University of California-Berkeley will integrate the previously developed PowerBlade wireless AC plug-through meters to measure real, reactive, and apparent power with load monitoring based on extracting high-fidelity electrical waveform features to capture power profiles and automatically identify and categorize MELs in a scalable manner. PowerBlade is currently the smallest, lowest cost, and lowest power AC plug-through meter. The team will develop three generations of highly accurate and cost-effective wireless smart power meters with 75% MELs coverage. These meters will develop high-resolution power profiles of MELs and the corresponding automatic identification and categorization of different MELs. The project will carry out lab testing to validate performance and provide feedback for further design improvements. Field evaluation will also be performed through two pilot studies to validate feasibility and analyze the data to yield a publically available dataset that details the power profile and identifies MELs, significantly advancing the understanding of MELs energy consumption trends, individual device power consumption patterns, and likely many other known and unknown topical areas related to MELs. This dataset will be valuable to both researchers investigating new opportunities to reduce MELs consumption through early-stage innovation, as well as utilities and decision-makers in developing their implementation strategies. Monitoring and identification of additional loads will also enable additional fault detection in controls optimization strategies.
Contacts
DOE Technology Manager: Erika Gupta and Harry Bergmann
Lead Performer: Prabal Dutta, University of California-Berkeley