Multi-agent control system with information fusion based comfort model for smart buildings
- 1. Department of Electrical Engineering and Computer Science, University of Toledo, 2801 Bancroft Rd., Toledo, OH 43606 (United States)
- 2. Department of Automation, Technological Educational Institute of Piraeus, 250 P. Ralli and Thivon Str., Egaleo 12244 (Greece)
Description
Highlights: ► Proposed a model to manage indoor energy and comfort for smart buildings. ► Developed a control system to maximize comfort with minimum energy consumption. ► Information fusion with ordered weighted averaging aggregation is used. ► Multi-agent technology and heuristic intelligent optimization are deployed in developing the control system. -- Abstract: From the perspective of system control, a smart and green building is a large-scale dynamic system with high complexity and a huge amount of information. Proper combination of the available information and effective control of the overall building system turns out to be a big challenge. In this study, we proposed a building indoor energy and comfort management model based on information fusion using ordered weighted averaging (OWA) aggregation. A multi-agent control system with heuristic intelligent optimization is developed to achieve a high level of comfort with the minimum power consumption. Case studies and simulation results are presented and discussed in this paper.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.apenergy.2012.05.020Additional details
Identifiers
- DOI
- 10.1016/j.apenergy.2012.05.020;
- PII
- S0306-2619(12)00368-6;
Publishing Information
- Journal Title
- Applied Energy
- Journal Volume
- 99
- Journal Page Range
- p. 247-254
- ISSN
- 0306-2619
- CODEN
- APENDX
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 45019837
- Subject category
- S42: ENGINEERING;
- Descriptors DEI
- AGGLOMERATION; BUILDINGS; CIVIL ENGINEERING; COMPUTERIZED SIMULATION; CONTROL SYSTEMS; ENERGY CONSUMPTION; ENVIRONMENTAL ENGINEERING; OPTIMIZATION
- Descriptors DEC
- ENGINEERING; SIMULATION
Optional Information
- Copyright
- Copyright (c) 2012 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.