How to cite this paper
Nesmachnow, S., Rossit, D., Toutouh, J & Luna, F. (2021). An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes.International Journal of Industrial Engineering Computations , 12(4), 365-380.
Refrences
Antunes, C. H., Soares, A., & Gomes, Á. (2017). An Integrated Building Energy Management System. In Mediterranean Green Buildings & Renewable Energy (pp. 191-199). Springer, Cham.
Barbato, A., & Capone, A. (2014). Optimization models and methods for demand-side management of residential users: A survey. Energies, 7(9), 5787-5824..
Bilil, H., Aniba, G., & Gharavi, H. (2016). Dynamic appliances scheduling in collaborative microgrids system. IEEE Transactions on Power Systems, 32(3), 2276-2287.
Calvillo, C. F., Sánchez-Miralles, A., & Villar, J. (2016). Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews, 55, 273-287.
Chavat, J. P., Graneri, J., & Nesmachnow, S. (2019). Household energy disaggregation based on pattern consumption similarities. In Ibero-American Congress on Information Management and Big Data (pp. 54-69). Springer, Cham.
Chavat, J. P., Graneri, J., & Nesmachnow, S. (2020a). Nonintrusive energy disaggregation by detecting similarities in consumption patterns. Revista Facultad de Ingeniería Universidad de Antioquía, 98, 27-46.
Chavat, J. P., Graneri, J., Alvez, G., & Nesmachnow, S. (2020b). ECD-UY: Detailed household electricity consumption dataset of Uruguay. Scientific Data, submitted manuscript.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2002). Evolutionary algorithms for solving multi-objective problems. Springer.
Colacurcio, G., Nesmachnow, S., Toutouh, J., Luna, F., & Rossit, D. (2019). Multiobjective household energy planning using evolutionary algorithms. In Ibero-American Congress on Information Management and Big Data (pp. 269-284). Springer, Cham.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. 3rd ed. MIT press.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
Eurostat Statistic Explained. Energy consumption in households. https://ec.europa.eu/eurostat/statistics-explained/index.php? title=Energy_consumption_in_households (Accessed: 12th June 2020).
Kolter, J. Z., & Johnson, M. J. (2011, August). REDD: A public data set for energy disaggregation research. In Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA (Vol. 25, No. Citeseer, pp. 59-62).
Luján, E., Otero, A., Valenzuela, S., Mocskos, E., Steffenel, L. A., & Nesmachnow, S. (2018). Cloud computing for smart energy management (CC-SEM project). In: Ibero-American Congress on Information Management and Big Data (pp. 116-131). Springer, Cham.
Lujan, E., Otero, A., Valenzuela, S., Mocskos, E., Steffenel, L., & Nesmachnow, S. (2020). An integrated platform for smart energy management: the CC-SEM project. Revista Facultad de Ingeniería Universidad de Antioquía, 97, 41–55.
Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., Naim, S., Abasi, A. K., & Alyasseri, Z. A. A. (2019). Optimization methods for power scheduling problems in smart home: Survey. Renewable and Sustainable Energy Reviews, 115, 109362.
Muhsen, D. H., Haider, H. T., Al-Nidawi, Y. M., & Khatib, T. (2019). Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. Sustainable Cities and Society, 50, 101651.
Nesmachnow, S. (2014). An overview of metaheuristics: accurate and efficient methods for optimisation. International Journal of Metaheuristics, 3, 320–347.
Nesmachnow, S., Baña, S., & Massobrio, R. (2017). 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, 2(5), 1–18.
Nesmachnow, S., & Iturriaga, S. (2019). Cluster-UY: Collaborative scientific high performance computing in Uruguay. In: International Conference on Supercomputing in Mexico (pp. 188-202). Series Communications in Computer and Information Science, Springer, Cham.
Orsi, E., & Nesmachnow, S. (2017). Smart home energy planning using IoT and the cloud. In 2017 IEEE URUCON (pp. 1-4). IEEE.
Pamulapati, T., Mallipeddi, R., & Lee, M. (2020). Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling. Applied Energy, 267, 114690.
Rabbani, M., Mohammadi, S., & Mobini, M. (2018). Optimum design of a CCHP system based on Economical, energy and environmental considerations using GA and PSO. International Journal of Industrial Engineering Computations, 9(1), 99-122.
Scott, E. O., & Luke, S. (2019). ECJ at 20: toward a general metaheuristics toolkit. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1391-1398.
Soares, A., Antunes, C., Oliveira, & C., Gomes, A. (2014a). A multi-objective genetic approach to domestic load scheduling in an energy management system. Energy , 77, 144–152.
Soares, A., Gomes, A., & Antunes, C. (2014b). Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions. Renewable and Sustainable Energy Reviews, 30, 490–503.
Soares, A., Gomes, Á., & Antunes, C. (2015). Integrated management of energy resources in the residential sector using evolutionary computation: a Review. In Soft Computing Applications for Renewable Energy and Energy Efficiency (pp. 320-347). IGI Global.
Soares, A., Gomes, Á., Antunes, C. H., & Cardoso, H. (2013, April). Domestic load scheduling using genetic algorithms. In European Conference on the Applications of Evolutionary Computation (pp. 142-151). Springer, Berlin, Heidelberg.
Turner, W., & Doty, S. (2007). Energy management handbook. The Fairmont Press.
U.S. Energy Information Administration (EIA), Energy use in homes. https://www.eia.gov/ (Accessed: 12th June 2020).
Yang, P., Tang, G., & Nehorai, A. (2012). A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems, 28(2), 884-892.
Barbato, A., & Capone, A. (2014). Optimization models and methods for demand-side management of residential users: A survey. Energies, 7(9), 5787-5824..
Bilil, H., Aniba, G., & Gharavi, H. (2016). Dynamic appliances scheduling in collaborative microgrids system. IEEE Transactions on Power Systems, 32(3), 2276-2287.
Calvillo, C. F., Sánchez-Miralles, A., & Villar, J. (2016). Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews, 55, 273-287.
Chavat, J. P., Graneri, J., & Nesmachnow, S. (2019). Household energy disaggregation based on pattern consumption similarities. In Ibero-American Congress on Information Management and Big Data (pp. 54-69). Springer, Cham.
Chavat, J. P., Graneri, J., & Nesmachnow, S. (2020a). Nonintrusive energy disaggregation by detecting similarities in consumption patterns. Revista Facultad de Ingeniería Universidad de Antioquía, 98, 27-46.
Chavat, J. P., Graneri, J., Alvez, G., & Nesmachnow, S. (2020b). ECD-UY: Detailed household electricity consumption dataset of Uruguay. Scientific Data, submitted manuscript.
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2002). Evolutionary algorithms for solving multi-objective problems. Springer.
Colacurcio, G., Nesmachnow, S., Toutouh, J., Luna, F., & Rossit, D. (2019). Multiobjective household energy planning using evolutionary algorithms. In Ibero-American Congress on Information Management and Big Data (pp. 269-284). Springer, Cham.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. 3rd ed. MIT press.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
Eurostat Statistic Explained. Energy consumption in households. https://ec.europa.eu/eurostat/statistics-explained/index.php? title=Energy_consumption_in_households (Accessed: 12th June 2020).
Kolter, J. Z., & Johnson, M. J. (2011, August). REDD: A public data set for energy disaggregation research. In Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA (Vol. 25, No. Citeseer, pp. 59-62).
Luján, E., Otero, A., Valenzuela, S., Mocskos, E., Steffenel, L. A., & Nesmachnow, S. (2018). Cloud computing for smart energy management (CC-SEM project). In: Ibero-American Congress on Information Management and Big Data (pp. 116-131). Springer, Cham.
Lujan, E., Otero, A., Valenzuela, S., Mocskos, E., Steffenel, L., & Nesmachnow, S. (2020). An integrated platform for smart energy management: the CC-SEM project. Revista Facultad de Ingeniería Universidad de Antioquía, 97, 41–55.
Makhadmeh, S. N., Khader, A. T., Al-Betar, M. A., Naim, S., Abasi, A. K., & Alyasseri, Z. A. A. (2019). Optimization methods for power scheduling problems in smart home: Survey. Renewable and Sustainable Energy Reviews, 115, 109362.
Muhsen, D. H., Haider, H. T., Al-Nidawi, Y. M., & Khatib, T. (2019). Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. Sustainable Cities and Society, 50, 101651.
Nesmachnow, S. (2014). An overview of metaheuristics: accurate and efficient methods for optimisation. International Journal of Metaheuristics, 3, 320–347.
Nesmachnow, S., Baña, S., & Massobrio, R. (2017). 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, 2(5), 1–18.
Nesmachnow, S., & Iturriaga, S. (2019). Cluster-UY: Collaborative scientific high performance computing in Uruguay. In: International Conference on Supercomputing in Mexico (pp. 188-202). Series Communications in Computer and Information Science, Springer, Cham.
Orsi, E., & Nesmachnow, S. (2017). Smart home energy planning using IoT and the cloud. In 2017 IEEE URUCON (pp. 1-4). IEEE.
Pamulapati, T., Mallipeddi, R., & Lee, M. (2020). Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling. Applied Energy, 267, 114690.
Rabbani, M., Mohammadi, S., & Mobini, M. (2018). Optimum design of a CCHP system based on Economical, energy and environmental considerations using GA and PSO. International Journal of Industrial Engineering Computations, 9(1), 99-122.
Scott, E. O., & Luke, S. (2019). ECJ at 20: toward a general metaheuristics toolkit. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1391-1398.
Soares, A., Antunes, C., Oliveira, & C., Gomes, A. (2014a). A multi-objective genetic approach to domestic load scheduling in an energy management system. Energy , 77, 144–152.
Soares, A., Gomes, A., & Antunes, C. (2014b). Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions. Renewable and Sustainable Energy Reviews, 30, 490–503.
Soares, A., Gomes, Á., & Antunes, C. (2015). Integrated management of energy resources in the residential sector using evolutionary computation: a Review. In Soft Computing Applications for Renewable Energy and Energy Efficiency (pp. 320-347). IGI Global.
Soares, A., Gomes, Á., Antunes, C. H., & Cardoso, H. (2013, April). Domestic load scheduling using genetic algorithms. In European Conference on the Applications of Evolutionary Computation (pp. 142-151). Springer, Berlin, Heidelberg.
Turner, W., & Doty, S. (2007). Energy management handbook. The Fairmont Press.
U.S. Energy Information Administration (EIA), Energy use in homes. https://www.eia.gov/ (Accessed: 12th June 2020).
Yang, P., Tang, G., & Nehorai, A. (2012). A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems, 28(2), 884-892.