Optimización del consumo eléctrico mediante la heurística cúmulo de partículas

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 eléc...

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Autores:: Pérez Camacho, Blanca Nydia
González Calleros, Juan Manuel
Rodríguez Gómez, Gustavo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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dc.title.spa.fl_str_mv	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
dc.title.translated.eng.fl_str_mv	Electrical consumption optimization through particle swarm optimization
title	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
spellingShingle	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas Consumo eléctrico Optimización del consumo Cúmulo de partículas Perfil de uso Perfil de consumo Electrical consumption Optimized consumption Particle swarm optimization User behavior Consumption behavior
title_short	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
title_full	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
title_fullStr	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
title_full_unstemmed	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
title_sort	Optimización del consumo eléctrico mediante la heurística cúmulo de partículas
dc.creator.fl_str_mv	Pérez Camacho, Blanca Nydia González Calleros, Juan Manuel Rodríguez Gómez, Gustavo
dc.contributor.author.none.fl_str_mv	Pérez Camacho, Blanca Nydia González Calleros, Juan Manuel Rodríguez Gómez, Gustavo
dc.contributor.orcid.spa.fl_str_mv	González Calleros, Juan Manuel [0000-0002-9661-3615] Pérez Camacho, Blanca Nydia [0000-0002-2334-8806] Rodríguez Gómez, Gustavo [0000-0002-4925-8892]
dc.subject.spa.fl_str_mv	Consumo eléctrico Optimización del consumo Cúmulo de partículas Perfil de uso Perfil de consumo
topic	Consumo eléctrico Optimización del consumo Cúmulo de partículas Perfil de uso Perfil de consumo Electrical consumption Optimized consumption Particle swarm optimization User behavior Consumption behavior
dc.subject.keywords.eng.fl_str_mv	Electrical consumption Optimized consumption Particle swarm optimization User behavior Consumption behavior
description	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.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-09-13
dc.date.accessioned.none.fl_str_mv	2024-09-11T21:57:41Z
dc.date.available.none.fl_str_mv	2024-09-11T21:57:41Z
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dc.identifier.issn.spa.fl_str_mv	ISSN: 1657-2831 e-ISSN: 2539-2115
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dc.relation.references.none.fl_str_mv	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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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
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spelling	Pérez Camacho, Blanca Nydia49299ff9-849f-4b16-9293-3b36af983106González Calleros, Juan Manuel7ca30aef-23ae-4f83-894b-08bea20855e2Rodríguez Gómez, Gustavoedb400ad-28c0-4536-b6e0-634f8cbbc2c5González Calleros, Juan Manuel [0000-0002-9661-3615]Pérez Camacho, Blanca Nydia [0000-0002-2334-8806]Rodríguez Gómez, Gustavo [0000-0002-4925-8892]2024-09-11T21:57:41Z2024-09-11T21:57:41Z2021-09-13ISSN: 1657-2831e-ISSN: 2539-2115http://hdl.handle.net/20.500.12749/26466instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/25392115.4293En 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.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.application/pdfspaUniversidad Autónoma de Bucaramanga UNABhttps://revistas.unab.edu.co/index.php/rcc/article/view/4293/3504https://revistas.unab.edu.co/index.php/rcc/issue/view/276Adika, 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.2013.2271427Barbato, 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/s12053-014-9269-3Blecic, 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-zChen, 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.2017.1600699CMClerc, 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/10.1109/4235.985692Emara, 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.2004.1383892Hao, 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.2017.8243232Huang, 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.2015.05.032Javaid, 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/en10081131Javaid, 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.2017.06.009Jiang, 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.2006.10.005Kadirkamanathan, 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.2005.857077Kakran, 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-2017-0187Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks. https://doi.org/10.1109/ICNN.1995.488968Kim, 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/s10846-006-9047-3Lotfi, 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.2017.80Muhammad 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.2018.00063Nadeem, 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/en11040888Rahim, 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.2016.08.008Rasheed, 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/en9070542Sun, 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.2017.8244020Tan, 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.2012.6269417Trelea, 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)00447-7van 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.2005.02.003Yao, 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.2016.31Zeng, 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.2010.46Zhigang 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.2008.4592994Zhou, 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.2016.03.047Vol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 14-21Consumo eléctricoOptimización del consumoCúmulo de partículasPerfil de usoPerfil de consumoElectrical consumptionOptimized consumptionParticle swarm optimizationUser behaviorConsumption behaviorOptimización del consumo eléctrico mediante la heurística cúmulo de partículasElectrical consumption optimization through particle swarm optimizationinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/access_right/c_abf2ORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf422721https://repository.unab.edu.co/bitstream/20.500.12749/26466/1/Art%c3%adculo.pdfac4f108b92afbc3c21897e6fa8d5e089MD51open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg9859https://repository.unab.edu.co/bitstream/20.500.12749/26466/3/Art%c3%adculo.pdf.jpg1e1df25a3d6611b6ffb7c750ce870caeMD53open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8347https://repository.unab.edu.co/bitstream/20.500.12749/26466/2/license.txt855f7d18ea80f5df821f7004dff2f316MD52open access20.500.12749/26466oai:repository.unab.edu.co:20.500.12749/264662024-09-11 22:01:28.097open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGEgUmV2aXN0YSBDb2xvbWJpYW5hIGRlIENvbXB1dGFjacOzbiBlcyBmaW5hbmNpYWRhIHBvciBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgQnVjYXJhbWFuZ2EuIEVzdGEgUmV2aXN0YSBubyBjb2JyYSB0YXNhIGRlIHN1bWlzacOzbiB5IHB1YmxpY2FjacOzbiBkZSBhcnTDrWN1bG9zLiBQcm92ZWUgYWNjZXNvIGxpYnJlIGlubWVkaWF0byBhIHN1IGNvbnRlbmlkbyBiYWpvIGVsIHByaW5jaXBpbyBkZSBxdWUgaGFjZXIgZGlzcG9uaWJsZSBncmF0dWl0YW1lbnRlIGludmVzdGlnYWNpw7NuIGFsIHDDumJsaWNvIGFwb3lhIGEgdW4gbWF5b3IgaW50ZXJjYW1iaW8gZGUgY29ub2NpbWllbnRvIGdsb2JhbC4=

Optimización del consumo eléctrico mediante la heurística cúmulo de partículas

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