Thermal Comfort – SFBA Chapter Event

Stefano Schiavon, Thomas Parkinson, Nathaniel Jones, Ingrid Chaires
Date of Recording:
Long Description

This is a recording of a San Francisco Bay Area Chapter event, held on May 28, 2019. Please note that there were audio problems with the first presentation. If you’d like to skip to where to audio starts, please forward to 12:15.

Presentation 1: The accuracy of the PMV/PPD model and on what to do in simulations

Presentation 2Defining acceptable temperature ranges without PMV

Presentation 3: Designing for thermal comfort in transient and non-uniform thermal environments – Case Study

More Information on Presentations:

Presentation 1: The accuracy of the PMV/PPD model and on what to do in simulations

The Predicted Mean Vote (PMV) and associated Predicted Percentage of Dissatisfied (PPD) are the mostly used thermal comfort models. We used the ASHRAE Global Thermal Comfort Database II to evaluate its prediction accuracy. We found that PMV predictions of thermal sensation were only correct one-third of the time, and had a mean absolute error of one unit on the thermal sensation scale. The accuracy of its predictions decreased towards the ends of the thermal sensation scale, meaning it performs poorly when it is most needed. People felt neutral or slightly cool/warm across a wider range of conditions than predicted by PMV, highlighting large potential for HVAC energy savings. The PPD was unable to predict the level of occupant dissatisfaction. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop better-performing thermal comfort models. For demonstration, we developed a simple model based on air temperature and its accuracy was higher than PMV on this database.

PresenterStefano Schiavon, PhD, is Associate Professor of Architecture at UC Berkeley and Associate Director of CEDR.
Stefano’s research is focused on finding ways to reduce energy consumption in buildings while improving occupant health, well-being and productivity. He received a PhD in Energy Engineering and an MS in Mechanical Engineering from the University of Padova, Italy.

Presentation 2: Defining acceptable temperature ranges without PMV

European thermal comfort standards use Predicted Mean Vote (PMV) classes as the basis of compliance criteria. The implicit assumption is that a narrower PMV range ensures higher thermal acceptability. However, our analysis of the ASHRAE Global Thermal Comfort Database II demonstrates that PMV classes are not appropriate design compliance criteria. We derived acceptable temperature ranges based on field studies in buildings and found they are wider (7.4K – 12.2 K) than the current standards mandate (2K – 6K). Our findings support the call for a relaxation of suggested temperature ranges in thermal comfort standards so as to minimize unnecessary space conditioning. These results, in conjunction with the analysis of PMV accuracy, reinforce the need for a new approach to predict and assess thermal comfort at different phases of building design and operation.

PresenterThomas Parkinson, PhD, is Postdoctoral Scholar at the Center for the Built Environment at UC Berkeley
Tom is working on topics that aim to enhance occupant comfort and well-being in buildings. This includes interests ranging from thermal physiology, indoor environmental quality, application of sensor technologies to built environments, and psychophysics. He received his PhD in Architectural Science from the University of Sydney.

Presentation 3: Designing for thermal comfort in transient and non-uniform thermal environments – Case Study

Human thermal comfort is a key design parameter for buildings. Analyzing thermal comfort can be complex, especially in transient and non-uniform thermal environments. This presentation previews a new set of tools that simulate human response to these environments. This allows for the analysis of thermal comfort under environmental conditions such as stratification, radiant asymmetry, and the effects of personal environmental controls, to name a few. The presentation describes aspects of the new simulation tools and their applications in design. A case study of an airport will be presented to showcase how the analysis of transient environments gave the design team confidence to relax comfort set points, leading to expected savings in energy consumption.

Nathaniel Jones, PhD Advanced Technology & Research Analyst at Arup
Nathaniel is a building scientist, educator, and software developer. He is currently an Advanced Technology & Research analyst at Arup and teaches building science at the University of San Francisco. His background spans architectural design, engineering, and computer science, with an emphasis on tools that aid informed decision making in early design. Nathaniel is active in the building science community and serves as chair of the International Building Performance Simulation Association-USA (IBPSA-USA) chapter’s Research Committee and subcommittee on Emerging Simulation Technologies. He is the author of multiple journal and conference papers related to building energy and daylighting simulation, and he is the developer of Accelerad and other early design simulation tools.

Ingrid Chaires, PE, Advanced Technology & Research Senior Mechanical Engineer at Arup
Ingrid is a senior mechanical engineer, and digital leader at Arup in the Advanced Technology & Research team, specializing in non-linear analysis, performance-based design, and data. Through the use of computational fluid dynamics, energy modeling, and data analytics, Ingrid provides consulting services to help clients achieve their energy, sustainability, and performance goals. Ingrid has led several research projects developing a more comprehensive approach to thermal comfort and its application in design for a range of physiological, environmental, and space use conditions. Outside of Arup, Ingrid is also on the board of the IBPSA-USA SFBA chapter.

thermal comfort, PPV, PMV, Predicted Mean Vote, Predicted Percentage of Dissatisfied, energy modeling, BEM, Indoor Thermal Quality Performance Prediction