Optimizing household energy planning in smart cities: A multiobjective approach
DOI:
https://doi.org/10.17533/udea.redin.20200587Keywords:
smart cities, household energy planning, evolutionary computation, multiobjective optimization, mixed integer programmingAbstract
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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References
W. Turner and S. Doty, Energy management handbook. The Fairmont Press, 2007.
A. Soares, A. Gomes, C. Antunes, and H. Cardoso, “Domestic load scheduling using genetic algorithms,” in European Conference on the Applications of Evolutionary Computation, 2013, pp. 142–151.
A. Soares, C. Antunes, C. Oliveira, and A. Gomes, “A multiobjective genetic approach to domestic load scheduling in an energy management system,” Energy, vol. 77, December 1 2014. [Online]. Available: https://doi.org/10.1016/j.energy.2014.05.101
M. Deakin and H. Al Waer, “From intelligent to smart cities,” Intelligent Buildings International, vol. 3, no. 3, July 2011. [Online]. Available: https://doi.org/10.1080/17508975.2011.586671
(2019) Energy use in homes. U.S. Energy Information Administration (EIA). Accessed Nov. 2019. [Online]. Available: https://bit.ly/2YjN5NG
(2019) Energy consumption in households. Eurostat Statistic Explained. Accessed Nov. 2019. [Online]. Available: https://bit.ly/2APLx63
G. Colacurcio, S. Nesmachnow, J. Toutouh, F. Luna, and D. G. Rossit, “Multiobjective household energy planning using evolutionary algorithms,” in Ibero-American Congress on Information Management and Big Data, Soria, Spain, 2019, pp. 269–284.
E. Luján and et al., “Cloud computing for smart energy management (CC-SEM Project),” in Ibero-American Congress on Information Management and Big Data, Soria, Spain, 2018, pp. 116–131.
E. Luján and et al., “An integrated platform for smart energy management: the CC-SEM project,” Revista Facultad de Ingeniería Universidad de Antioquia, no. 97, November 05 2019. [Online]. Available: https://doi.org/10.17533/udea.redin.20191147
I. S. Bayram and T. S. Ustun, “A survey on behind the meter energy management systems in smart grid,” Renewable and Sustainable Energy Reviews, vol. 72, May 2017. [Online]. Available: https://doi.org/10.1016/j.rser.2016.10.034
S. Nesmachnow, S. Baña, and R. Massobrio, “A distributed platform for big data analysis in smart cities: combining intelligent transportation systems and socioeconomic data for Montevideo, Uruguay,” EAI Endorsed Transactions on Smart Cities, vol. 2, no. 5, 2017. [Online]. Available: https://doi.org/10.4108/eai.19-12-2017.153478
J. Kolter and M. Johnson, “Redd: A public data set for energy disaggregation research,” in Workshop on Data Mining Applications in Sustainability, San Diego, CA, USA, 2011, pp. 59–62.
J. Chavat, J. Graneri, and S. Nesmachnow, “Energy disaggregation of household appliances based on pattern consumption similarities,” in Iberoamerican Congress on Smart Cities, 2019.
F. Glover, “An improved MIP formulation for products of discrete and continuous variables,” Journal of Information and Optimization Sciences, vol. 5, no. 1, 1984. [Online]. Available: https://doi.org/10.1080/02522667.1984.10698780
A. Soares, A. Gomes, and C. H. Antunes, “Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions,” Renewable and Sustainable Energy Reviews, vol. 30, February 2014. [Online]. Available: https://doi.org/10.1016/j.rser.2013.10.019
E. Orsi and S. Nesmachnow, “Smart home energy planning using IoT and the cloud,” in IEEE URUCON, Montevideo, Uruguay, 2017.
H. Bilil, G. Aniba, and H. Gharavi, “Dynamic appliances scheduling in collaborative microgrids system,” IEEE Transactions on Power Systems, vol. 32, no. 3, May 2017. [Online]. Available: https://doi.org/10.1109/TPWRS.2016.2613479
Z. W. Geem and Y. Yoon, “Harmony search optimization of renewable energy charging with energy storage system,” International Journal of Electrical Power & Energy Systems, vol. 86, March 2017. [Online]. Available: https://doi.org/10.1016/j.ijepes.2016.04.028
A. Barbato and A. Capone, “Optimization models and methods for demand-side management of residential users: A survey,” Energies, vol. 7, September 2014. [Online]. Available: https://doi.org/10.3390/en7095787
X. Guan, Z. Xu, and Q. S. Jia, “Energy-efficient buildings facilitated by microgrid,” IEEE Transactions on smart grid, vol. 1, no. 3, December 2010. [Online]. Available: https://doi.org/10.1109/TSG.2010.2083705
A. Barbato, A. Capone, L. Chen, F. Martignon, and S. Paris, “A distributed demand-side management framework for the smart grid,” Computer Communications, vol. 57, February 15 2015. [Online]. Available: https://doi.org/10.1016/j.comcom.2014.11.001
S. Nesmachnow, “An overview of metaheuristics: accurate and efficient methods for optimisation,” International Journal of Metaheuristics, vol. 3, no. 4, December 2014. [Online]. Available: https://doi.org/10.1504/IJMHEUR.2014.068914
Nesmachnow, S., “Computación científica de alto desempeño en la facultad de ingeniería, universidad de la república,” Revista de la Asociación de Ingenieros del Uruguay, vol. 61, no. 1, pp. 12–15, 2010.
L. A. Wolsey and G. L. Nemhauser, Integer and combinatorial optimization. Hoboken, NJ, USA: John Wiley & Sons, 2014.
Gurobi optimizer reference manual. Gurobi Optimization. Accessed Jun. 12, 2020. [Online]. Available: https://bit.ly/3hqg1wd
W. E. Hart and et al., Pyomo-optimization modeling in python, 2nd ed. Berlin, Germany: Springer Science & Business Media, 2017.
G. Colacurcio, “Algoritmos evolutivos para la planificación de eficiencia energética en hogares,” undergraduate thesis, Faculty of Engineering, Universidad de la República, Montevideo, Uruguay, 2019, in Press.
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