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

University College Cork

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	AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages	1371-1376
Number of pages	6
Publication status	Published - 2011
Event	25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States Duration: 7 Aug 2011 → 11 Aug 2011

Publication series

Name	Proceedings of the National Conference on Artificial Intelligence
Volume	2

Conference

Conference	25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
Country/Territory	United States
City	San Francisco, CA
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

Mady, A. E. D., Provan, G. M., Ryan, C., & Brown, K. N. (2011). Stochastic model predictive controller for the integration of building use and temperature regulation. In AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference (pp. 1371-1376). (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).

Mady, Alie El Din ; Provan, Gregory M. ; Ryan, Conor et al. / Stochastic model predictive controller for the integration of building use and temperature regulation. AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference. 2011. pp. 1371-1376 (Proceedings of the National Conference on Artificial Intelligence).

@inproceedings{fdd206da010741a8946ca993c5db38e2,

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.\}",

year = "2011",

language = "English",

isbn = "9781577355090",

series = "Proceedings of the National Conference on Artificial Intelligence",

pages = "1371--1376",

booktitle = "AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference",

note = "25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 ; Conference date: 07-08-2011 Through 11-08-2011",

}

Mady, AED, Provan, GM, Ryan, C & Brown, KN 2011, Stochastic model predictive controller for the integration of building use and temperature regulation. in AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference. Proceedings of the National Conference on Artificial Intelligence, vol. 2, pp. 1371-1376, 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11, San Francisco, CA, 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.
AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference. 2011. p. 1371-1376 (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).

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

Y1 - 2011

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/80055047943

M3 - Conference proceeding

AN - SCOPUS:80055047943

SN - 9781577355090

T3 - Proceedings of the National Conference on Artificial Intelligence

SP - 1371

EP - 1376

BT - AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference

T2 - 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11

Y2 - 7 August 2011 through 11 August 2011

ER -

Mady AED, Provan GM, Ryan C, Brown KN. Stochastic model predictive controller for the integration of building use and temperature regulation. In AAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference. 2011. p. 1371-1376. (Proceedings of the National Conference on Artificial Intelligence).

Stochastic model predictive controller for the integration of building use and temperature regulation

Abstract

Publication series

Conference

UN SDGs

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