Title

Virtual models of indoor-air-quality sensors

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2010

Journal/Book/Conference Title

Applied Energy

Abstract

A data-driven approach for modeling indoor-air-quality (IAQ) sensors used in heating, ventilation, and air conditioning (HVAC) systems is presented. The IAQ sensors considered in the paper measure three basic parameters, temperature, CO2, and relative humidity. Three models predicting values of IAQ parameters are built with various data mining algorithms. Four data mining algorithms have been tested on the HVAC data set collected at an office-type facility. The computational results produced by models built with different data mining algorithms are discussed. The neural network (NN) with multi-layer perceptron (MLP) algorithms produced the best results for all three IAQ sensors among all algorithms tested. The models built with data mining algorithms can serve as virtual IAQ sensors in buildings and be used for on-line monitoring and calibration of the IAQ sensors. The approach presented in this paper can be applied to HVAC systems in buildings beyond the type considered in this paper. 2009 Elsevier Ltd. All rights reserved.

Keywords

Sustainability

Published Article/Book Citation

Applied Energy, 87:6 (2010) pp.2087-2094.

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URL

http://ir.uiowa.edu/mie_pubs/209