Optimizing household energy planning in smart cities: A multiobjective approach

Keywords: Smart cities, household energy planning, evolutionary computation, multiobjective optimization, mixed integer programming


This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning problem considering user preferences, which in turn are proposed to be solved using a multiobjective evolutionary approach, in order to minimize energy consumption and maximize quality of service offered to users. Promising results are reported on realistic instances of the problem, compared with situations where no intelligent energy planning are used (i.e., ‘Bussiness as Usual’ model) and also with a greedy algorithm developed in the framework of the reference project. The proposed evolutionary approach was able to improve up to 29.0% in energy utilization and up to 65,3% in user preferences over the reference methods

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Author Biographies

Sergio Nesmachnow, Universidad de la República

Works as a full professor at the Faculty of Engineering of Universidad de la República, Uruguay

Giovanni Colacurcio, Universidad de la República

Giovanni Colacurcio is an engineering student at the Faculty of Engineering of Universidad de la República, Uruguay.

Diego Gabriel Rossit, Universidad Nacional del Sur (UNS)

Works at the Department of Engineering at Universidad Nacional del Sur and at CONICET. His field of study is Operations Research and Reverse Logistics.

Jamal Toutouh, Massachusetts Institute of Technology

Jamal Toutouh is currently a postdoctoral Marie Skłodowska-Curie fellow at MIT (Massachusetts Institute of Technology). He works in ALFA (Anyscale Learning For All) research group at CSAIL (Computer Science and Artificial Intelligence Laboratory). Jamal does research in Co-/Evolutionary Algorithms and Deep Learning to address Cybersecurity and Smart Citiy problems.Diego Gabriel Rossit currently works at the Department of Engineering at Universidad Nacional del Sur and at CONICET. His field of study is Operations Research and Reverse Logistics.

Francisco Luna, Universidad de Málaga

He is a researcher at the Department of Computer Sciences and Languages of the Universidad de Malaga.


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How to Cite
Nesmachnow S., Colacurcio G., Rossit D. G., Toutouh J., & Luna F. (2020). Optimizing household energy planning in smart cities: A multiobjective approach. Revista Facultad De Ingeniería Universidad De Antioquia, (101), 8-19. https://doi.org/10.17533/udea.redin.20200587