Title

Modeling and optimization of HVAC energy consumption

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2010

Journal/Book/Conference Title

Applied Energy

Abstract

A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%. 2010 Elsevier Ltd. All rights reserved.

Keywords

Sustainability

Published Article/Book Citation

Applied Energy, 87:10 (2010) pp.3092-3102.

This document is currently not available here.

Share

COinS
 

URL

http://ir.uiowa.edu/mie_pubs/128