Abstract
The European Union Establishes that for the next few years, a cleaner and more efficient energy system should be used. In order to achieve this, this work proposes an energy optimization method that facilitates the achievement of these objectives. Existing technologies allow us to create a system that optimizes the use of energy in homes and offers some type of benefit to its residents. Specifically, this study has developed a recommendation system based on a multiagent system that allows to obtain consumption data from electronic devices in a home, obtain information on electricity prices from the Internet, and provide recommendations based on consumption patterns of users and electricity prices. In this way, the system recommends new hours in which to use the appliances, offering the economic benefit that it would propose recommendations for the user. In this way, it is possible to distribute and optimize the use of energy in homes and reduce the peaks in electricity consumption. The system provides encouraging results in order to resolve the problem proposed by the European Union by optimizing the use of energy among different hours of the day and saving money for the customer.
Highlights
The European Union established in October of 2014 a series of requirements that should be complete by the year 2030
The recommendation systems based on collaborative filtering (CF) recommend items that have been seen, bought or positive-ranked by users with similar preferences to the target one
The hybrid filtering (HF) technique is used in recommendation systems that want to avoid the problems and limitations that CF and content-based filtering (CB) usually have
Summary
The European Union established in October of 2014 a series of requirements that should be complete by the year 2030. CB recommendation systems try to find similar items or similar users or whatever it is that we are recommending This technique finds one object similar to another one if both share some features and characteristics in common. The recommendation systems based on CF recommend items that have been seen, bought or positive-ranked by users with similar preferences to the target one. This technique categorizes two different items as similar if a great number of users, similar to the original one, buy or rate the two items. The users are seen as similar if they share a great number of preferences. It uses the best of both techniques in order to provide more accurate and effective recommendations
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