1. Predictive Control in the
Built Environment
Sponsor: Lars Junghans
Researchers: Joshua Johnston, Ekaterina Rakhmatulina
Abstract
Modern building methods are increasingly becoming more
focused on using different means to make the operating
systems in the built environment more efficient. There is
increasing work being done in the field of building
automation, also known as “smart buildings”. This project is
working towards creating a program that will automate most
of the major climate control systems in a building by using
predictive weather and current internal environment climate
data. In order to test this program, a scale model is to be
created which will simulate an office space in which different
measurements such as sunlight, radiation, and temperature
will take place. This model will be integrated with a scaled
down air flow system to simulate an actual room. Upon
completion, the program will be assimilated with the model
to truly test its ability for automation of the climate systems.
The programs outputs will be adjusted to fit the experimental
data. An effective system will allow for complete optimization
of climate control systems, saving operation costs and
decreasing waste energy. This system will not only save on
operating costs, but it will also reduce the amount of
unnecessary waste released into the environment as a result
of power generation.
Introduction
As the population grows, energy consumption continues to
increase. Although the world is not going to run out of
energy quite yet, it is important to optimize energy use. One
such way is to focus on the building sector. The US building
sector alone is responsible for 7% of the global energy
consumption, and it accounts for astonishing 41% of the US
total energy consumption. In the building sector itself,
around half of the energy consumption is due to space
heating, space cooling, and lighting. By studying how the
energy is being used in the building by its occupants,
predictive control and optimization models can be employed
to minimize energy use.
Algorithm
A computer algorithm was created in order to calculate heat
transfer through the wall. It was written in C++ language and
it receives various data inputs such as temperature, light
intensity, weather forecasts (via SOAP Client), number of
occupants, user preferences, etc. It will have a continuous
data input which will be analyzed to predict future
environmental conditions and adjust to the user preferences
while decreasing energy consumption. The algorithm is
based on thermodynamic principles of heat transfer.
Algorithm Output: visual of the heat
gradient in a wall at 3 different times Model
The physical model is made of a 30”x30” MDF cube on the
exterior, with a 18”x18” MDF Interior. There is 5” of R-30
insulation separating the interior from the exterior. The
purpose of this model is to simulate a room environment,
such that various data collection experiments can be
performed. Sensors and a heat source are placed inside the
model, and the temperature is analyzed over time to give
real heat transfer values. Collected data will be used to
optimize the algorithm.
Conclusion
There is still a lot more work to be done on this project.
Throughout the year, a strong base has been built for which
future work can go off of. A physical model for testing was
constructed and a working computer program was created
to be integrated with the collected data. Continued research
on this project is vital because this system has a great
potential to increase the efficiency of certain aspects of the
built environment, saving both energy and cost.
References:
National Digital Forecast Database (NDFD) Simple Object Access Protocol (SOAP) Web
Service (National Digital Forecast Database XML/SOAP
Service)http://graphical.weather.gov/xml/
Cengel, Yunus A., and Afshin J. Ghajar. "Ch 4: Transient Heat Conduction." Heat and Mass
Transfer: A Practical Approach. New York: McGraw-Hill, 2010. N. pag. Print.
Werner-Juszczuk, Anna, and Slawomir Soroko. "Application of Boundary Element
Method to Solution of Transient Heat Conduction." Acta Mechanica Et Automatica 6.4
(2012): 67-73. Web.
α=thermal diffusivity
k=thermal conductivity
ρ=density of material
Bi=Biot number
Τ=Fourier number
cp=specific heat
h=heat transfer coefficient
L=thickness of a wall segment
hw=height of wall
Ti=inside temperature
T∞=outside temperature
Q=rate of convection heat
transfer
Q=total heat transfer
Governing Equations:
Schematic of SOAP Client
Front View Back View CAD Model
Test Results for Physical Model