14,914 research outputs found
A multi-agent intelligent decision making support system for home energy management in smart grid: A fuzzy TOPSIS approach
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
Dynamic adaptation of user profiles in recommender systems
In a period of time in which the content available through the Internet
increases exponentially and is more easily accessible every day, techniques
for aiding the selection and extraction of important and personalised
information are of vital importance. Recommender Systems (RS) appear as
a tool to help the user in a decision making process by evaluating a set of
objects or alternatives and aiding the user at choosing which one/s of them
suits better his/her interests or preferences. Those preferences need to be
accurate enough to produce adequate recommendations and should be
updated if the user changes his/her likes or if they are incorrect or
incomplete. In this work an adequate model for managing user preferences
in a multi-attribute (numerical and categorical) environment is presented to
aid at providing recommendations in those kinds of contexts. The
evaluation process of the recommender system designed is supported by a
new aggregation operator (Unbalanced LOWA) that enables the
combination of the information that defines an alternative into a single
value, which then is used to rank the whole set of alternatives. After the
recommendation has been made, learning processes have been designed to
evaluate the user interaction with the system to find out, in a dynamic and
unsupervised way, if the user profile in which the recommendation process
relies on needs to be updated with new preferences. The work detailed in
this document also includes extensive evaluation and testing of all the
elements that take part in the recommendation and learning processes
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