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Development of an Inverse Method to Estimate Overall Building and Ventilation Parameters of Large Commercial Buildings
Journal article   Peer reviewed

Development of an Inverse Method to Estimate Overall Building and Ventilation Parameters of Large Commercial Buildings

T. A Reddy, S Deng, D. E Claridge and Agami T Reddy
Journal of solar energy engineering, v 121(1)
01 Feb 1999
DOI: https://doi.org/10.1115/1.2888141
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

We propose an inverse method to estimate building and ventilation parameters from non-intrusive monitoring of heating and cooling thermal energy use of large commercial buildings. The procedure involves first deducing the loads of an ideal one-zone building from the monitored data, and then in the framework of a mechanistic macro-model, using a multistep linear regression approach to determine the regression coefficients (along with their standard errors) which can be finally translated into estimates of the physical parameters (along with the associated errors). Several different identification schemes have been evaluated using heating and cooling data generated from a detailed building simulation program for two different building geometries and building mass at two different climatic locations. A multistep identification scheme has been found to yield very accurate results, and an explanation as to why it should be so is also given. This approach has been shown to remove much of the bias introduced in multiple linear regression approach with correlated regressor variables. We have found that the parameter identification process is very accurate when daily data over an entire year are used. Parameter identification accuracy using twelve monthly data points and daily data over three months of the year was also investigated. Identification with twelve monthly data points seems to be fairly accurate while that using daily data over a season does not yield very good results. This latter issue needs to be investigated further because of its practical relevance.

Metrics

9 Record Views
19 citations in Web of Science
21 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#7 Affordable and Clean Energy
#13 Climate Action
#11 Sustainable Cities and Communities

InCites Highlights

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Collaboration types
Domestic collaboration
Web of Science research areas
Energy & Fuels
Engineering, Mechanical
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