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Building Analytics Tool Deployment at Scale: Benefits, Costs, and Deployment Practices

Author

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  • Guanjing Lin

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Hannah Kramer

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Valerie Nibler

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Eliot Crowe

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Jessica Granderson

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

Abstract

Buildings are becoming more data-rich. Building analytics tools, including energy information systems (EIS) and fault detection and diagnostic (FDD) tools, have emerged to enable building operators to translate large amounts of time-series data into actionable findings to achieve energy and non-energy benefits. To expedite data analytics adoption and facilitate technology innovation, building owners, technology developers, and researchers need reliable cost–benefit data and evidence-based guidance on deployment practices. This paper fulfills these needs with the energy use and survey data from a wide-ranging research and industry partnership program that covers thousands of buildings installed with analytics tools. The paper indicates that after two years of implementation, organizations using FDD tools and EIS tools achieved 9% and 3% median annual energy savings, respectively. The median base cost and annual recurring cost for FDD are USD 0.65 per square meter (m 2 ) (USD 0.06 per square foot [ft 2 ]) and USD 0.22 per m 2 (USD 0.02 per ft 2 ), and are USD 0.11 per m 2 (USD 0.01 per ft 2 ) and USD 0.11 per m 2 (USD 0.01 per ft 2 ) for EIS. The common metrics and analyses that are used in the tools to support the discovery of energy efficiency measures are summarized in detail. Two best practice examples identified to maximize the benefits of tool implementation are also presented. Opportunities to advance the state of technology include simplified data integration and management, and more efficient processes for acting on analytics outputs. Compared with previous efforts in the literature, the findings presented in this paper demonstrate the effectiveness of building analytics tools with the largest known dataset.

Suggested Citation

  • Guanjing Lin & Hannah Kramer & Valerie Nibler & Eliot Crowe & Jessica Granderson, 2022. "Building Analytics Tool Deployment at Scale: Benefits, Costs, and Deployment Practices," Energies, MDPI, vol. 15(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4858-:d:854351
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    References listed on IDEAS

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    1. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
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    1. Guanjing Lin & Armando Casillas & Maggie Sheng & Jessica Granderson, 2023. "Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building," Energies, MDPI, vol. 16(20), pages 1-21, October.
    2. Antonio Rosato & Marco Savino Piscitelli & Alfonso Capozzoli, 2023. "Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings," Energies, MDPI, vol. 16(2), pages 1-6, January.

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