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  4. Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes
 
conference paper

Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes

Nghiem, Xuan Truong  
•
Jones, Colin
2017
2017 American Control Conference (ACC)
2017 American Control Conference

This paper presents an approach to provide demand response services with buildings. Each building receives a normalized signal that tells it to increase or decrease its power demand, and the building is free to implement any suitable strategy to follow the command, most likely by changing some of its setpoints. Due to this freedom, the proposed approach lowers the barrier for any buildings equipped with a reasonably functional building management system to participate in the scheme. The response of the buildings to the control signal is modeled by a Gaussian Process, which can predict the power demand of the buildings and also provide a measure of its confidence in the prediction. A battery is included in the system to compensate for this uncertainty and improve the demand response performance of the system. A model predictive controller is developed to optimally control the buildings and the battery, while ensuring their operational constraints with high probability. Our approach is validated by realistic co-simulations between Matlab and the building energy simulator EnergyPlus.

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Type
conference paper
DOI
10.23919/ACC.2017.7963394
Author(s)
Nghiem, Xuan Truong  
Jones, Colin
Date Issued

2017

Published in
2017 American Control Conference (ACC)
Start page

2919

End page

2924

Subjects

Gaussian Process

•

building modeling

•

building control

•

demand response

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LA3  
Event nameEvent placeEvent date
2017 American Control Conference

Seattle, Washington, USA

May 24-26, 2017

Available on Infoscience
February 9, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/134249
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