10.17533/udea.redin.20200587
https://zenodo.org/records/4574067
oai:zenodo.org:4574067
Sergio Nesmachnow
Sergio Nesmachnow
0000-0002-8146-4012
Universidad de la República
Giovanni Colacurcio
Giovanni Colacurcio
0000-0002-1489-0329
Universidad de la República
Diego Gabriel Rossit
Diego Gabriel Rossit
0000-0002-8531-445X
Universidad Nacional del Sur
Jamal Toutouh
Jamal Toutouh
0000-0003-1152-0346
Massachusetts Institute of Technology
Francisco Luna
Francisco Luna
0000-0002-0455-7223
Universidad de Málaga
Optimizing household energy planning in smart cities: A multiobjective approach
Zenodo
2020
Smart cities
Evolutionary computation
Multiobjective optimization
Mixed integer programming
2020-06-05
eng
Published
Creative Commons Attribution 4.0 International
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.
NesmachnowS., ColacurcioG., RossitD. G., ToutouhJ., & LunaF. (2020). Optimizing household energy planning in smart cities: A multiobjective approach. Revista Facultad De Ingeniería Universidad De Antioquia. https://doi.org/10.17533/udea.redin.20200587
European Commission
10.13039/501100000780
799078
NeCOL: An Innovative Methodology for Building Better Deep Learning Tools for Real Word Applications