Sianaki, Omid Ameri and Hussain, Omar and Dillon, Tharam S. and Tabesh, Azadeh Rajabian. 2010. Intelligent Decision Support System for Including Consumers’Preferences in Residential Energy Consumption Management in Smart Grid, in Yunus, S. and Yunus, J. (ed), 2nd International Conference on Computer Modelling and Simulation (CIMSim 2010), Sep 28 2010, pp. 154-159. Bali, Indonesia: IEEE.
Source TitleProceedings of the 2nd international conference on computer modelling and simulation (CIMSim 2010)
Source Conference2nd International Conference on Computer Modelling and Simulation (CIMSim 2010)
SchoolDigital Ecosystems and Business Intelligence Institute (DEBII)
RemarksCopyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright.
AbstractSmart Grid is a novel initiative the aim of which is to deliver energy to the users and also to achieve consumption efficiency by means of two-way communication. The Smart Grid architecture is a combination of various hardware devices, management and reporting software tools that are combined within an ICT infrastructure. This infrastructure is needed to make the smart grid sustainable, creative and intelligent. One of the main goals of Smart Grid is to achieve Demand Response(DR) by increasing the end users' participation in decision making and increasing the awareness that will lead them to manage their energy consumption in an efficient way. Approaches proposed in the literature achieve demand response at the different levels of the Smart Grid, but no approach focuses on the users' point of view at the home level on a continuous basis and in an intelligent way to achieve demand response. In this paper, we develop such an approach by which demand response can be achieved on a continuous basis at the home level. To achieve this, the dynamic notion of price will be utilized to develop an intelligent decision-making model that will assist the users in achieving demand response.
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