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Lookup NU author(s): Weixian Li
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© 1982-2012 IEEE.Smart buildings form an integral part of the smart grid that lowers electricity cost. This objective can be achieved by using suitable enhanced algorithms and techniques. The motivation of this paper is the request for an economical decision-making system from a commercial company in Singapore. This paper presents an economical building management system (EBMS) concept inspired by the building management system and machine learning. EBMS improves the decision-making process through rule-based algorithm within the system. Machine learning algorithm is included as part of EBMS to enhance the computational calculation for the electricity distribution. It includes the function of self-making decision for purchasing electricity based on forecasting system for electricity price or real-time electricity price data when facility managers are not present. EBMS uses multiagent system to communicate, interact, and negotiate with multiple agents for energy supply and demand in the building. Simulation studies have shown the potential of EBMS concept to provide a cost reduction solution up to 61.42% savings for smart buildings. Better electricity purchasing contract option can be decided based on the forecasting system for building power consumption.
Author(s): Li W
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Industrial Electronics
Year: 2020
Volume: 67
Issue: 5
Pages: 4235-4243
Online publication date: 19/06/2019
Acceptance date: 03/06/2019
ISSN (print): 0278-0046
ISSN (electronic): 1557-9948
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/TIE.2019.2922946
DOI: 10.1109/TIE.2019.2922946
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