Managing end-user preferences in the smart grid
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
CIMSIM '10 Proceedings of the 2010 Second International Conference on Computational Intelligence, Modelling and Simulation
An integer linear programming based optimization for home demand-side management in smart grid
ISGT '12 Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies
ISGT '12 Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies
Behaviometrics for multiple residents in a smart environment
Behaviometrics for multiple residents in a smart environment
Review: A state-of the-art survey of TOPSIS applications
Expert Systems with Applications: An International Journal
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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.