Open Access
Thermal Environmental Conditions for Human Occupancy
Standard Ashrae
- Vol. 5
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The article was published on 1992-01-01 and is currently open access. It has received 5855 citations till now. The article focuses on the topics: Occupancy.read more
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DR-Advisor: A data-driven demand response recommender system
TL;DR: In this article, a model-based control with regression trees algorithm (mbCRT) is proposed to perform closed-loop control for DR strategy synthesis for large commercial buildings, which leads to a curtailment of up to 380kW and over $45,000 in savings.
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Local variation of outdoor thermal comfort in different urban green spaces in Guangzhou, a subtropical city in South China
TL;DR: In this paper, the authors focused on developing a thermal comfort range and estimating neutral and preferred temperatures through questionnaire surveys and micro-climatic measurements, and found that preferred temperatures were lower than neutral temperatures among different sites, indicating the instinctive preference of people from relatively hot regions for a cooler thermal state.
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Study on human skin temperature and thermal evaluation in step change conditions: From non-neutrality to neutrality
TL;DR: In this article, the authors explore how the human body adapts to an environment as the temperature changes, and describe the relationship between the objective skin temperature and subjective thermal evaluation, and find that even if a poor thermal environment was improved slightly, the thermal satisfaction of subjects increased significantly.
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Performance assessment of buildings via post-occupancy evaluation: A case study of the building of the architecture and software engineering departments in Salahaddin University-Erbil, Iraq
TL;DR: In this article, the authors developed a post-occupancy evaluation (POE) framework that integrates building performance attributes for university buildings and facilities in the Iraqi Kurdistan region based on users' satisfaction.
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Can HVAC really learn from users? A simulation-based study on the effectiveness of voting for comfort and energy use optimization
TL;DR: The results indicate that employing a k-means machine learning technique enables the automatic configuration of an HVAC system to reduce energy consumption while keeping the majority of occupants within acceptable comfort levels.