Affiliations: [a] School of Information Systems, Curtin University, Perth, WA, Australia | [b] Department of Electrical and Computer Engineering, Curtin University, Perth, WA, Australia
Correspondence:
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Corresponding author: Omid Ameri Sianaki, School of Information Systems, Curtin University, Perth, WA, Australia. E-mail: [email protected]; [email protected]
Abstract: In the context of intelligent home energy management in smart grid, the occupants’ consumption behavior has a direct effect on the demand and supply of the electrical energy market. Correspondingly, the policies of the utility providers affect consumption behavior so techniques and tools are required to analyse the occupants’ preferences, habits and lifestyles in order to support and facilitate their decision-making regarding the curtailing of their energy consumption and costs. The uncertainty about householders’ preferences increases the uncertainty of appliance prioritization and makes it difficult to determine the consistency of preferences in terms of energy consumption. In this complex system, the preferences and judgments of householders are represented by linguistic and vague patterns. This paper proposes a much better representation of this linguistics that can be developed and refined by using the evaluation methods of fuzzy set theory. The proposed approach will apply the fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) for achieving preferences. Based on our detailed literature review of the multi-agent system approach in this field, it is expected that the proposal model will offer a robust tool for communication and decision-making between occupant agents and dynamic environmental variables. It is shown that the proposed fuzzy TOPSIS approach will enable and assist householders to maximize their participation in demand response programs.
Keywords: Multi-agent systems, demand response, dynamic real-time pricing, fuzzy TOPSIS, intelligent decision support system, home energy management, smart grid, smart home, scheduling model
