Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Sign In to gain access to subscriptions and/or personal tools.
Indoor and Built Environment
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Chao, C. Y.H.
Right arrow Articles by Hu, J. S.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Development of an Enthalpy and Carbon Dioxide Based Demand Control Ventilation for Indoor Air Quality and Energy Saving with Neural Network Control

Christopher Y.H. Chao

Department of Mechanical Engineering, The Hong Kong University of Science and Technology, meyhchao{at}ust.hk

J. S. Hu

Department of Mechanical Engineering, The Hong Kong University of Science and Technology

An enthalpy and carbon dioxide level based demand control ventilation (EDCV) algorithm has been developed. This takes into account both the indoor occupancy level and the energy content of the fresh air and return air while controlling the fresh air supply. It has been applied under various operating conditions to ensure that the most effective control strategy was used. A back propagation (BP) neural network was used to tune the proportional, integral and differential (PID) parameters in order to obtain a good control performance. Experiments were conducted in a mediumsized lecture theatre to verify the performance of the developed EDCV algorithm in a real application. The results showed that acceptable indoor air quality could be obtained with less energy consumption. Under the optimum experimental conditions, about 15.4% of the total cooling energy was saved. The control performance was found to be good with the PID parameters tuned via the neural network

Key Words: Indoor air quality • Demand control ventilation • Enthalpy • CO 2 • Neural network • Energy saving

Indoor and Built Environment, Vol. 13, No. 6, 463-475 (2004)
DOI: 10.1177/1420326X05047375
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?