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

Guanjing Lin, Hannah Kramer, Valerie Nibler, Eliot Crowe and Jessica Granderson
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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

Energies, 2022, vol. 15, issue 13, 1-17

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.

Keywords: building analytics; smart building; energy management; energy information system; fault detection and diagnostics; costs and benefits (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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