Prashanth Ganesh

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2019-51

May 16, 2019

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-51.pdf

In recent years, buildings have become a major source of global energy consumption. A significant portion of that can be attributed to the heating, ventilation, and air conditioning (HVAC) and electric lighting systems. With the advent of new smart building technologies that can, among other things, monitor environmental data, occupancy, and remotely control various building systems, it becomes possible to implement intelligent systems that can alleviate these issues. In this work, we propose a lightweight, decentralized controller that minimizes building energy consumption while maintaining occupant comfort by integrating the aforementioned smart building technologies with EnergyPlus simulation and programmatic building control in a model-predictive control loop. We demonstrate its efficacy on two real-world implementations in the CREST lab in Cory Hall and illustrate that with a short data collection period, it is rapidly deployable and can offer significant energy savings and presents extensions to differential privacy while achieving the desired comfort objectives.

Advisors: Costas J. Spanos

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BibTeX citation:

@mastersthesis{Ganesh:EECS-2019-51,
    Author= {Ganesh, Prashanth},
    Title= {A Model-Predictive Controller for Joint Smart Building Systems},
    School= {EECS Department, University of California, Berkeley},
    Year= {2019},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-51.html},
    Number= {UCB/EECS-2019-51},
    Abstract= {In recent years, buildings have become a major source of global energy consumption. A significant portion of that can be attributed to the heating, ventilation, and air conditioning (HVAC) and electric lighting systems. With the advent of new smart building technologies that can, among other things, monitor environmental data, occupancy, and remotely control various building systems, it becomes possible to implement intelligent systems that can alleviate these issues. In this work, we propose a lightweight, decentralized controller that minimizes building energy consumption while maintaining occupant comfort by integrating the aforementioned smart building technologies with EnergyPlus simulation and programmatic building control in a model-predictive control loop. We demonstrate its efficacy on two real-world implementations in the CREST lab in Cory Hall and illustrate that with a short data collection period, it is rapidly deployable and can offer significant energy savings and presents extensions to differential privacy while achieving the desired comfort objectives.},
}

EndNote citation:

%0 Thesis
%A Ganesh, Prashanth 
%T A Model-Predictive Controller for Joint Smart Building Systems
%I EECS Department, University of California, Berkeley
%D 2019
%8 May 16
%@ UCB/EECS-2019-51
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-51.html
%F Ganesh:EECS-2019-51