Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV-Reference-Cited by-同舟云学术

Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV

Published:2018-05 Issue: Volume:166 Page:229-238
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Zampetti L.,Arnesano M.,Revel G.M.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference36 articles.

1. A review on buildings energy consumption information;Pérez-Lombard;Energy Build.,2008

2. Modernising building energy codes to secure our global energy future;Saheb,2011

3. Development and validation of a low-cost infrared measurement system for real-time monitoring of indoor thermal comfort;Revel;Meas. Sci. Technol.,2014

4. Development and experimental evaluation of a thermography measurement system for real-time monitoring of comfort and heat rate exchange in the built environment;Revel;Meas. Sci. Technol.,2012

5. Cost-effective technologies to control indoor air quality and comfort in energy efficient building retrofitting;Revel;Environ. Eng. Manag. J.,2015

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