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Comparison of Ice-on-Coil Thermal Energy Storage Models

Published

Author(s)

Kalyan Ram Kanagala, Amanda Pertzborn

Abstract

Data collected from the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) are used to develop a physics-based and four machine learning models of ice-on-coil thermal energy storage (TES): linear interpolation, linear regression, neural network, and Gaussian process. Data cleaning considerations are discussed in addition to presenting the results of the five models. For this TES system, which is linear over a significant range of operation, the linear interpolation model performs the best, but there is a thorough discussion of the advantages and disadvantages of each model.
Citation
Technical Note (NIST TN) - 2265
Report Number
2265

Keywords

HVAC, machine learning, thermal storage.

Citation

Kanagala, K. and Pertzborn, A. (2023), Comparison of Ice-on-Coil Thermal Energy Storage Models, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2265, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956501 (Accessed May 27, 2024)

Issues

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Created September 13, 2023