Neural network and polynomial approximated thermal comfort models for HVAC systems-Reference-Cited by-同舟云学术

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Neural network and polynomial approximated thermal comfort models for HVAC systems

Published:2013-01 Issue: Volume:59 Page:107-115
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Castilla M.,Álvarez J.D.,Ortega M.G.,Arahal M.R.

Funder

Andalusia Regional Government (Consejería de Economía, Innovación y Ciencia)

European Union through the European Regional Development Fund (ERDF)

Spanish Ministry of Science and Innovation

EU-ERDF

Spanish Ministry of Education

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference39 articles.

1. Application of artificial neural network to predict the optimal start time for heating system in building;Yang;Energy Convers Manag,2003

2. Advanced fuzzy logic controllers design and evaluation for buildings' occupant thermal-visual comfort and indoor air quality satisfaction;Kolokotsa;Energy Build,2001

3. Donaisky E, Oliveira G, Freire Z, Mendes N. PMV-based predictive algorithms for controlling thermal comfort in building plants. In: Proceedings of the 16th IEEE international conference on control applications, Singapore; 2007, p. 182–87.

4. Freire R, Oliveira G, Mendes N. Thermal comfort based predictive controllers for building heating systems. IFAC workshop on energy saving control in plants and buildings, Bansko, Bulgaria; 2006.

5. Dumur D, Boucher P, Murphy K, Déqué F. Comfort control in residential housing using predictive controllers. In: Proceedings of the 1997 IEEE international conference on control applications, Hartford, Connecticut, U.S.A; 1997, p. 265–70.

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