Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions-Reference-Cited by-同舟云学术

Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions

Published:2017-03 Issue: Volume:114 Page:1-10
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Dai Changzhi,Zhang Hui,Arens Edward,Lian Zhiwei

Funder

National Science Foundation of the United States

key project of National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference34 articles.

1. Standard 55-2013: Thermal Environmental Conditions for Human Occupancy;ASHRAE,2013

2. CEN15251: Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics,2007

3. Air quality and thermal comfort in office Buildings: results of a large indoor environmental quality survey;Huizenga;Proc. Heal. Build. III,2006

4. Research of the room occupant complaining behavior pattern for the indoor environmental control;Mao;Adv. Mater. Res.,2011

5. User-adaptable comfort control for HVAC systems;Federspiel;Am. Control Conf.,1992

Cited by 124 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identification and application of the best-suited machine learning algorithm based on thermal comfort data characteristic: A data-driven approach;Journal of Building Engineering;2024-10

2. Exploring the thermally-controlled fentanyl transdermal therapy to provide constant drug delivery by physics-based digital twins;European Journal of Pharmaceutical Sciences;2024-09

3. Integrating Internet of Things (IoT) Approach to Post-Occupancy Evaluation (POE): An Experimental At-the-Moment Occupant Comfort Control System;Buildings;2024-07-09

4. Dual-phase prediction model of passenger thermal sensation using facial thermal imaging and environmental factors;Case Studies in Thermal Engineering;2024-06

5. Assessing children's outdoor thermal comfort with facial expression recognition: An efficient approach using machine learning;Building and Environment;2024-06