Adaptation of fan motor and VFD efficiency correlations using Bayesian inference

Rivalin, Lisa; Pritoni, Marco; Stabat, Pascal; Marchio, Dominique

doi:10.1080/23744731.2019.1571869

Adaptation of fan motor and VFD efficiency correlations using Bayesian inference

Journal Article · Tue May 14 00:00:00 EDT 2019 · Science and Technology for the Built Environment

DOI:https://doi.org/10.1080/23744731.2019.1571869· OSTI ID:1603522

^[1]; Pritoni, Marco ^[2]; Stabat, Pascal ^[3]; Marchio, Dominique ^[3]

Engie Axima, Nantes (France); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Mines ParisTech, PSL (France)

Energy performance contracts (EPC) are types of agreements in which a service provider guarantees that customers’ buildings will achieve a specified energy performance (i.e., minimum energy savings) to reduce the risk of their investment in energy efficiency improvements. EPC requires prediction of future energy consumption of the building, at the design stage, before construction or major retrofit. To this end, building energy simulations taking into account all the major energy-using components are performed. In particular, fans can contribute significantly to the total building consumption. The overall efficiency of fans is the combination of three factors: mechanical, motor, and variable frequency drive (VFD). Manufacturers usually provide fan mechanical efficiency curves for a broad operating range. In contrast, motor and VFD efficiencies are generally given at rating conditions only. To represent part-load conditions, correlations are typically used to estimate motor and VFD efficiency variations, to evaluate the overall electricity consumption. The first aim of this study is to evaluate existing correlations for motor and VFD efficiency as a function of load and speed, by comparison with manufacturer data, for a vendor that has shared its detailed test data. While VFD efficiency correlations from the literature provide reasonable accuracy against real data, motor correlations under predict actual motor efficiency at low loads. Finally, the second aim of the paper is to improve such correlations using Bayesian inference to fit the available data.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1603522

Journal Information:: Science and Technology for the Built Environment, Journal Name: Science and Technology for the Built Environment Journal Issue: 7 Vol. 25; ISSN 2374-4731

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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