A Simplified Correlation Method for Cooling Energy Calculation in High-rise Commercial Buildings

Young Sub Kim, Kang Soo Kim

Research output: Contribution to journalArticle

Abstract

For economic evaluation of cooling plant equipment in the preliminary design stage, it is necessary to simplify energy prediction method, which should include the efficiency corrected by part-load ratio. This study proposed a simplified correlation method with regression equations of time-average partial loads and chiller capacity. DOE-2 program was used to carry out a parametric study of twelve variables. Five input variables were considered to be significant and were used in the regression equations. To test the accuracy of this method, calculated results were compared with DOE-2 simulated results. Testing showed good agreement between these two results with 5.9–7.6% error. It is hoped that this method can be used as an easy prediction tool for comparing energy use of different cooling plants during the early design stage.

Original languageEnglish
Pages (from-to)107-113
Number of pages7
JournalJournal of Asian Architecture and Building Engineering
Volume1
Issue number2
DOIs
Publication statusPublished - 2002

Fingerprint

Correlation methods
building
Cooling
energy
regression
Economics
Testing
efficiency
Rise
Energy
evaluation
economics
Stage Design
Prediction
Equations

Keywords

  • Chilled water temperature
  • Condenser water temperature
  • Part-load ratio
  • Regression analysis

ASJC Scopus subject areas

  • Architecture
  • Civil and Structural Engineering
  • Cultural Studies
  • Building and Construction
  • Arts and Humanities (miscellaneous)

Cite this

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abstract = "For economic evaluation of cooling plant equipment in the preliminary design stage, it is necessary to simplify energy prediction method, which should include the efficiency corrected by part-load ratio. This study proposed a simplified correlation method with regression equations of time-average partial loads and chiller capacity. DOE-2 program was used to carry out a parametric study of twelve variables. Five input variables were considered to be significant and were used in the regression equations. To test the accuracy of this method, calculated results were compared with DOE-2 simulated results. Testing showed good agreement between these two results with 5.9–7.6{\%} error. It is hoped that this method can be used as an easy prediction tool for comparing energy use of different cooling plants during the early design stage.",
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