Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation

Alie El Din Mady; Gregory M. Provan; Conor Ryan; Kenneth N. Brown

Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation

Alie El Din Mady
, Gregory M. Provan
, Conor Ryan
, Kenneth N. Brown

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

The aim of a modern Building Automation System (BAS) is to enhance interactive control strategies for energy efficiency and user comfort. In this context, we develop a novel control algorithm that uses a stochastic building occupancy model to improve mean energy efficiency while minimizing expected discomfort. We compare by simulation our Stochastic Model Predictive Control (SMPC) strategy to the standard heating control method to empirically demonstrate a 4.3% reduction in energy use and 38.3% reduction in expected discomfort.

Original language	English
Title of host publication	Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011
Publisher	AAAI Press
Pages	1371-1376
Number of pages	6
ISBN (Electronic)	9781577355083
Publication status	Published - 11 Aug 2011
Event	25th AAAI Conference on Artificial Intelligence, AAAI 2011 - San Francisco, United States Duration: 7 Aug 2011 → 11 Aug 2011

Publication series

Name	Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011

Conference

Conference	25th AAAI Conference on Artificial Intelligence, AAAI 2011
Country/Territory	United States
City	San Francisco
Period	7/08/11 → 11/08/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 12 Responsible Consumption and Production

Cite this

@inproceedings{572c1c50b6dd4d51a474f5ed7df6a154,

title = "Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation",

abstract = "The aim of a modern Building Automation System (BAS) is to enhance interactive control strategies for energy efficiency and user comfort. In this context, we develop a novel control algorithm that uses a stochastic building occupancy model to improve mean energy efficiency while minimizing expected discomfort. We compare by simulation our Stochastic Model Predictive Control (SMPC) strategy to the standard heating control method to empirically demonstrate a 4.3\% reduction in energy use and 38.3\% reduction in expected discomfort.",

author = "Mady, \{Alie El Din\} and Provan, \{Gregory M.\} and Conor Ryan and Brown, \{Kenneth N.\}",

note = "Publisher Copyright: Copyright {\textcopyright} 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 25th AAAI Conference on Artificial Intelligence, AAAI 2011 ; Conference date: 07-08-2011 Through 11-08-2011",

year = "2011",

month = aug,

day = "11",

language = "English",

series = "Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011",

publisher = "AAAI Press",

pages = "1371--1376",

booktitle = "Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011",

}

Mady, AED, Provan, GM, Ryan, C & Brown, KN 2011, Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation. in Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011. Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011, AAAI Press, pp. 1371-1376, 25th AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, United States, 7/08/11.

Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation. / Mady, Alie El Din; Provan, Gregory M.; Ryan, Conor et al.
Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011. AAAI Press, 2011. p. 1371-1376 (Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

TY - GEN

T1 - Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation

AU - Mady, Alie El Din

AU - Provan, Gregory M.

AU - Ryan, Conor

AU - Brown, Kenneth N.

PY - 2011/8/11

Y1 - 2011/8/11

N2 - The aim of a modern Building Automation System (BAS) is to enhance interactive control strategies for energy efficiency and user comfort. In this context, we develop a novel control algorithm that uses a stochastic building occupancy model to improve mean energy efficiency while minimizing expected discomfort. We compare by simulation our Stochastic Model Predictive Control (SMPC) strategy to the standard heating control method to empirically demonstrate a 4.3% reduction in energy use and 38.3% reduction in expected discomfort.

AB - The aim of a modern Building Automation System (BAS) is to enhance interactive control strategies for energy efficiency and user comfort. In this context, we develop a novel control algorithm that uses a stochastic building occupancy model to improve mean energy efficiency while minimizing expected discomfort. We compare by simulation our Stochastic Model Predictive Control (SMPC) strategy to the standard heating control method to empirically demonstrate a 4.3% reduction in energy use and 38.3% reduction in expected discomfort.

UR - https://www.scopus.com/pages/publications/85167591873

M3 - Conference proceeding

AN - SCOPUS:85167591873

T3 - Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011

SP - 1371

EP - 1376

BT - Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011

PB - AAAI Press

T2 - 25th AAAI Conference on Artificial Intelligence, AAAI 2011

Y2 - 7 August 2011 through 11 August 2011

ER -

Stochastic Model Predictive Controller for the Integration of Building Use and Temperature Regulation

Abstract

Publication series

Conference

UN SDGs

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