April 6, 2017
The mitigation of discomfort in hot summertime conditions remains one of the greatest sources of occupant dissatisfaction and energy waste in contemporary buildings. Most of us can recall experiences of overly-cold indoor conditions in the height of summer but few understand how such cultural practices evolved, why they have been sustained, and how to change them.
This session will explain the origin of some of these practices using a simple Honeybee energy model along with Ladybug thermal comfort models and graphics. Several methods for improving these practices will be proposed, simulated, visualized and explained, including several control strategies (ie, collective user-driven setpoints, warmer air with individual desk fans), along with building envelope strategies (automated shades, electrochromic glass, frit, exterior overhangs/louvers, etc.)
Date & Time: Thu, Apr 6, 2017; 9:30 AM - 11:30 AM PDT
Learn More & Register: https://attendee.gototraining.com/42b11/register/6748379437041510658
** IBPSA-USA members save $10 on Performance.Network trainings, which reduces the non-member $15 registration fee to $5. Just use the code ibpsaus. Learn more here. **
About the Presenter:
Chris Mackey is a building scientist and designer at Payette Architects as well as a recent graduate of MIT’s Masters of Architecture and Masters of Science in Building Technology
programs. His completed thesis involved the development of software to produce high-resolution thermal comfort maps of buildings off of EnergyPlus results as well as a new suite of spatial thermal comfort metrics that mirror those currently used to quantify daylight.
SKILLS & TOPICS: Chris has intimate knowledge of the following languages / software: Python, Grasshopper, Rhino (incl. RhinoCommon), EnergyPlus, OpenStudio. In his free time, Chris is an avid contributor to the Ladybug + Honeybee environmental analysis plugins for grasshopper and is currently in the process of building a new “insect” to link these two plugins and the Rhino/Grasshopper interface to macro-scale climate modeling engines and data sets.
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