A self-validation method for regression models of unitary HVAC equipment based on manufacturers' data

Huojun Yang, Haorong Li, David P. Yuill

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Regression models of various types are often used to predict the performance of unitary HVAC systems. Models must be validated before they can be used with any confidence. This article describes a self-validation method for regression models based on the widely-available manufacturer's performance data. The self-validation method also uses the manufacturer's data, thus avoiding the problem of scarce or expensive laboratory test validation data. It is derived based on the Fundamental Theorem of Calculus. In this method, the discrete manufacturer's data are studied within the context of the continuous domain, so that the partial derivatives of the regression models derived from the manufacturer's data are approximated for each independent variable. The derivative of a regression model is said to be effectively validated when all of its partial derivatives are valid, and then the quality of the regression model can be further evaluated for its intended use. The self-validation method was implemented to validate models of unitary HVAC equipment that were developed with Generic Rating-Data-Based modeling, and it showed that self-validation using the manufacturer's data is consistent with laboratory data based validation.

Original languageEnglish (US)
Pages (from-to)175-192
Number of pages18
JournalHVAC and R Research
Volume19
Issue number2
DOIs
StatePublished - Feb 1 2013

ASJC Scopus subject areas

  • Building and Construction

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