Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
| dc.contributor.author | Pérez Camacho, Blanca Nydia | |
| dc.contributor.author | González Calleros, Juan Manuel | |
| dc.contributor.author | Rodríguez Gómez, Gustavo | |
| dc.contributor.orcid | González Calleros, Juan Manuel [0000-0002-9661-3615] | spa |
| dc.contributor.orcid | Pérez Camacho, Blanca Nydia [0000-0002-2334-8806] | spa |
| dc.contributor.orcid | Rodríguez Gómez, Gustavo [0000-0002-4925-8892] | spa |
| dc.date.accessioned | 2024-09-11T21:57:41Z | |
| dc.date.available | 2024-09-11T21:57:41Z | |
| dc.date.issued | 2021-09-13 | |
| dc.description.abstract | En el presente trabajo se da una breve explicación de la técnica de optimización por cúmulo de partículas para ser implementada como parte de la búsqueda del estado óptimo de consumo de un conjunto de dispositivos. Los dispositivos de uso doméstico, en conjunto, permiten caracterizar el consumo eléctrico de una casa habitación a través del comportamiento de uso. Cada uno de los dispositivos presenta un comportamiento de consumo. El objetivo de la optimización se refleja en la función objetivo, la cual es definida de acuerdo con el propósito general de implementación. Los datos de consumo de los dispositivos eléctricos son almacenados en vectores de consumo-hora, donde cada una de las posiciones corresponde al consumo generado por un dispositivo en una hora determinada. Cada uno de los vectores es usado por la heurística como un vector de referencia durante la búsqueda para encontrar el vector que cumple con la función objetivo. | spa |
| dc.description.abstractenglish | This paper gives a brief explanation of the particle swarm optimization technique, which is given to be implemented to look for the optimal state of consumption from a set of household appliances. The household appliances allow characterizing the electrical consumption of a dwelling house through use behavior. Every household appliance shows a behavior consumption. The goal optimization objective is seen as the objective function defined according to the general implementation purpose. The consumption data of household appliances are stored in hourly consumption vectors, where everyone's position corresponds to the consumption generated by a household appliance in each hour. The heuristics use each of the vectors as a reference vector during the search to find the vector that fulfills the objective function. | eng |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.doi | https://doi.org/10.29375/25392115.4293 | |
| dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.identifier.issn | ISSN: 1657-2831 | spa |
| dc.identifier.issn | e-ISSN: 2539-2115 | spa |
| dc.identifier.repourl | repourl:https://repository.unab.edu.co | spa |
| dc.identifier.uri | http://hdl.handle.net/20.500.12749/26466 | |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.relation | https://revistas.unab.edu.co/index.php/rcc/article/view/4293/3504 | spa |
| dc.relation.references | Adika, C. O., & Wang, L. (2014). Autonomous Appliance Scheduling for Household Energy Management. IEEE Transactions on Smart Grid, 5(2). https://doi.org/10.1109/TSG.2013.2271427 | |
| dc.relation.references | Barbato, A., Capone, A., Carello, G., Delfanti, M., Falabretti, D., & Merlo, M. (2014). A framework for home energy management and its experimental validation. Energy Efficiency, 7(6). https://doi.org/10.1007/s12053-014-9269-3 | |
| dc.relation.references | Blecic, I., Cecchini, A., & Trunfio, G. A. (2007). A decision support tool coupling a causal model and a multi-objective genetic algorithm. Applied Intelligence, 26(2). https://doi.org/10.1007/s10489-006-0009-z | |
| dc.relation.references | Chen, S., Liu, T., Gao, F., Ji, J., Xu, Z., Qian, B., Wu, H., & Guan, X. (2017). Butler, Not Servant: A Human-Centric Smart Home Energy Management System. IEEE Communications Magazine, 55(2). https://doi.org/10.1109/MCOM.2017.1600699CM | |
| dc.relation.references | Clerc, M., & Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1). https://doi.org/10.1109/4235.985692 | |
| dc.relation.references | Emara, H. M., & Abdel Fattah, H. A. (2004). Continuous swarm optimization technique with stability analysis. Proceedings of the 2004 American Control Conference, 2811–2817. https://doi.org/10.23919/ACC.2004.1383892 | |
| dc.relation.references | Hao, Y., Wang, W., & Qi, Y. (2017, October). Optimal home energy management with PV system in time of use tariff environment. 2017 Chinese Automation Congress (CAC). https://doi.org/10.1109/CAC.2017.8243232 | |
| dc.relation.references | Huang, Y., Tian, H., & Wang, L. (2015). Demand response for home energy management system. International Journal of Electrical Power & Energy Systems, 73. https://doi.org/10.1016/j.ijepes.2015.05.032 | |
| dc.relation.references | Javaid, N., Hussain, S., Ullah, I., Noor, M., Abdul, W., Almogren, A., & Alamri, A. (2017). Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations. Energies, 10(8). https://doi.org/10.3390/en10081131 | |
| dc.relation.references | Javaid, N., Naseem, M., Rasheed, M. B., Mahmood, D., Khan, S. A., Alrajeh, N., & Iqbal, Z. (2017). A new heuristically optimized Home Energy Management controller for smart grid. Sustainable Cities and Society, 34. https://doi.org/10.1016/j.scs.2017.06.009 | |
| dc.relation.references | Jiang, M., Luo, Y. P., & Yang, S. Y. (2007). Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters, 102(1). https://doi.org/10.1016/j.ipl.2006.10.005 | |
| dc.relation.references | Kadirkamanathan, V., Selvarajah, K., & Fleming, P. J. (2006). Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Transactions on Evolutionary Computation, 10(3), 245–255. https://doi.org/10.1109/TEVC.2005.857077 | |
| dc.relation.references | Kakran, S., & Chanana, S. (2018). Energy Scheduling of Smart Appliances at Home under the Effect of Dynamic Pricing Schemes and Small Renewable Energy Source. International Journal of Emerging Electric Power Systems, 19(2). https://doi.org/10.1515/ijeeps-2017-0187 | |
| dc.relation.references | Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks. https://doi.org/10.1109/ICNN.1995.488968 | |
| dc.relation.references | Kim, D. H., & Shin, S. (2006). Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm. Journal of Intelligent and Robotic Systems, 46(2). https://doi.org/10.1007/s10846-006-9047-3 | |
| dc.relation.references | Lotfi, J., Abdi, F., & Abbou, M. F. (2017, November). Smart Home Energy System Modeling and Implementation. 2017 European Conference on Electrical Engineering and Computer Science (EECS). https://doi.org/10.1109/EECS.2017.80 | |
| dc.relation.references | Muhammad Mohsin, S., Javaid, N., Madani, S. A., Abbas, S. K., Akber, S. M. A., & Khan, Z. A. (2018, May). Appliance Scheduling in Smart Homes with Harmony Search Algorithm for Different Operation Time Intervals. 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA). https://doi.org/10.1109/WAINA.2018.00063 | |
| dc.relation.references | Nadeem, Z., Javaid, N., Malik, A., & Iqbal, S. (2018). Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes. Energies, 11(4). https://doi.org/10.3390/en11040888 | |
| dc.relation.references | Rahim, S., Javaid, N., Ahmad, A., Khan, S. A., Khan, Z. A., Alrajeh, N., & Qasim, U. (2016). Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy and Buildings, 129. https://doi.org/10.1016/j.enbuild.2016.08.008 | |
| dc.relation.references | Rasheed, M., Javaid, N., Awais, M., Khan, Z., Qasim, U., Alrajeh, N., Iqbal, Z., & Javaid, Q. (2016). Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes. Energies, 9(7). https://doi.org/10.3390/en9070542 | |
| dc.relation.references | Sun, X., Ji, S., & Wen, C. (2017, October). An optimized scheduling strategy for smart home users under the limitation of daily electric charge. 2017 Chinese Automation Congress (CAC). https://doi.org/10.1109/CAC.2017.8244020 | |
| dc.relation.references | Tan, X., Shan, B., Hu, Z., & Wu, S. (2012, June). Study on demand side management decision supporting system. 2012 IEEE International Conference on Computer Science and Automation Engineering. https://doi.org/10.1109/ICSESS.2012.6269417 | |
| dc.relation.references | Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6). https://doi.org/10.1016/S0020-0190(02)00447-7 | |
| dc.relation.references | van den Bergh, F., & Engelbrecht, A. P. (2006). A study of particle swarm optimization particle trajectories. Information Sciences, 176(8). https://doi.org/10.1016/j.ins.2005.02.003 | |
| dc.relation.references | Yao, L., Shen, J.-Y., & Lim, W. H. (2016, December). Real-Time Energy Management Optimization for Smart Household. 2016 IEEE International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.31 | |
| dc.relation.references | Zeng, W., Zhang, Y., & Yan, L. (2010, October). Mechanism of Particle Swarm Optimization and Analysis on Its Convergence. 2010 Third International Symposium on Information Processing. https://doi.org/10.1109/ISIP.2010.46 | |
| dc.relation.references | Zhigang Lian, Fan Zhu, Zailin Guan, & Xinyu Shao. (2008). The analysis of particle swarm optimization algorithm’s convergence. 2008 7th World Congress on Intelligent Control and Automation. https://doi.org/10.1109/WCICA.2008.4592994 | |
| dc.relation.references | Zhou, B., Li, W., Chan, K. W., Cao, Y., Kuang, Y., Liu, X., & Wang, X. (2016). Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews, 61. https://doi.org/10.1016/j.rser.2016.03.047 | |
| dc.relation.uri | https://revistas.unab.edu.co/index.php/rcc/issue/view/276 | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.source | Vol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 14-21 | spa |
| dc.subject | Consumo eléctrico | spa |
| dc.subject | Optimización del consumo | spa |
| dc.subject | Cúmulo de partículas | spa |
| dc.subject | Perfil de uso | spa |
| dc.subject | Perfil de consumo | spa |
| dc.subject.keywords | Electrical consumption | eng |
| dc.subject.keywords | Optimized consumption | eng |
| dc.subject.keywords | Particle swarm optimization | eng |
| dc.subject.keywords | User behavior | eng |
| dc.subject.keywords | Consumption behavior | eng |
| dc.title | Optimización del consumo eléctrico mediante la heurística cúmulo de partículas | spa |
| dc.title.translated | Electrical consumption optimization through particle swarm optimization | eng |
| dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.driver | info:eu-repo/semantics/article | |
| dc.type.hasversion | info:eu-repo/semantics/publishedVersion | |
| dc.type.local | Artículo | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART |
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