As the energy consumption from residential and commercial buildings makes up approximately
three-quarters of the U.S. electricity loads, analyzing building energy consumption behavior becomes essential for effective power grid operation. An accurate physics-based building energy simulator that is built on first principles can predict an individual building’s energy response, such as energy consumption and indoor environmental conditions under different weather and operational control scenarios. In the building energy simulator, several parameters that specify building characteristics need to be set a priori. Among those parameters, some parameters are season-dependent, whereas other parameters should be globally employed throughout a year. Existing studies in parameter calibration ignore such heterogeneity, which causes suboptimal calibration results. This study presents a new calibration approach that considers the seasonal dependency.
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Multiblock Parameter Calibration in Computer Models
Parameter calibration aims to estimate unobservable parameters used in a computer model by using physical process responses and computer model outputs. In the literature, existing studies calibrate all parameters simultaneously using an entire data set. However, in certain applications, some parameters are associated with only a subset of data. For example, in the building energy simulation, cooling (heating) season parameters should be calibrated using data collected during the cooling (heating) season only. This study provides a new multiblock calibration approach that considers such heterogeneity. Unlike existing studies that build emulators for the computer model response, such as the widely used Bayesian calibration approach, we consider multiple loss functions to be minimized, each for a block of parameters that use the corresponding data set, and estimate the parameters using a nonlinear optimization technique. We present the convergence properties under certain conditions and quantify the parameter estimation uncertainties. The superiority of our approach is demonstrated through numerical studies and a real-world building energy simulation case study.
History: Bianca Maria Colosimo served as the senior editor for this article.
Funding: This work was partially supported by the National Science Foundation [Grants CMMI-1662553, CMMI-2226348, and CBET-1804321].
Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/8623151/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0029 ).
more » « less- Award ID(s):
- 2226348
- PAR ID:
- 10481380
- Publisher / Repository:
- INFORMS
- Date Published:
- Journal Name:
- INFORMS Journal on Data Science
- Volume:
- 2
- Issue:
- 2
- ISSN:
- 2694-4022
- Page Range / eLocation ID:
- 116 to 137
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/ijds.2023.0029
