Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system

Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry exp...

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Autores:
Anupong, Wongchai
Muda, Iskandar
Auda AbdulAmeer, Sabah
Al-Kharsan, Ibrahim H.
Alviz Meza, Anibal
Cardenas Escorcia, Yulineth
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10405
Acceso en línea:
https://hdl.handle.net/11323/10405
https://repositorio.cuc.edu.co/
Palabra clave:
Building
Energy optimization
ACS
SVM
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_f2ce6abfa5dab180df427a845a70df28
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10405
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
title Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
spellingShingle Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
Building
Energy optimization
ACS
SVM
title_short Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
title_full Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
title_fullStr Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
title_full_unstemmed Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
title_sort Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system
dc.creator.fl_str_mv Anupong, Wongchai
Muda, Iskandar
Auda AbdulAmeer, Sabah
Al-Kharsan, Ibrahim H.
Alviz Meza, Anibal
Cardenas Escorcia, Yulineth
dc.contributor.author.none.fl_str_mv Anupong, Wongchai
Muda, Iskandar
Auda AbdulAmeer, Sabah
Al-Kharsan, Ibrahim H.
Alviz Meza, Anibal
Cardenas Escorcia, Yulineth
dc.subject.proposal.eng.fl_str_mv Building
Energy optimization
ACS
SVM
topic Building
Energy optimization
ACS
SVM
description Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO2 production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO2 is reduced to negligible levels.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-23T21:27:10Z
dc.date.available.none.fl_str_mv 2023-08-23T21:27:10Z
dc.date.issued.none.fl_str_mv 2023-02-08
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.spa.fl_str_mv Anupong, W.; Muda, I.; AbdulAmeer, S.A.; Al-Kharsan, I.H.; Alviz-Meza, A.; Cárdenas-Escrocia, Y. Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System. Sustainability 2023, 15, 3118. https://doi.org/10.3390/ su15043118
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10405
dc.identifier.doi.none.fl_str_mv 10.3390/su15043118
dc.identifier.eissn.spa.fl_str_mv 2071-1050
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Anupong, W.; Muda, I.; AbdulAmeer, S.A.; Al-Kharsan, I.H.; Alviz-Meza, A.; Cárdenas-Escrocia, Y. Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System. Sustainability 2023, 15, 3118. https://doi.org/10.3390/ su15043118
10.3390/su15043118
2071-1050
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10405
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Sustainability
dc.relation.references.spa.fl_str_mv 1. Molajou, A.; Afshar, A.; Khosravi, M.; Soleimanian, E.; Vahabzadeh, M.; Variani, H.A. A New Paradigm of Water, Food, and Energy Nexus. Environ. Sci. Pollut. Res. 2021, 28, 1–11. [CrossRef] [PubMed]
2. Molajou, A.; Pouladi, P.; Afshar, A. Incorporating Social System into Water-Food-Energy Nexus. Water Resour. Manag. 2021, 35, 4561–4580. [CrossRef]
3. Ahmadi, M.H.; Sayyaadi, H.; Dehghani, S.; Hosseinzade, H. Designing a Solar Powered Stirling Heat Engine Based on Multiple Criteria: Maximized Thermal Efficiency and Power. Energy Convers. Manag. 2013, 75, 282–291. [CrossRef]
4. Al Doury, R.R.J.; Salem, T.K.; Nazzal, I.T.; Kumar, R.; Sadeghzadeh, M. A Novel Developed Method to Study the Energy/Exergy Flows of Buildings Compared to the Traditional Method. J. Therm. Anal. Calorim. 2021, 145, 1151–1161. [CrossRef]
5. Ahmadi, M.H.; Jashnani, H.; Chau, K.W.; Kumar, R.; Rosen, M.A. Carbon Dioxide Emissions Prediction of Five Middle Eastern Countries Using Artificial Neural Networks. Energy Sources A Recovery Util. Environ. Eff. 2019, 41, 1–13. [CrossRef]
6. Yavari, F.; Salehi Neyshabouri, S.A.; Yazdi, J.; Molajou, A.; Brysiewicz, A. A Novel Framework for Urban Flood Damage Assessment. Water Resour. Manag. 2022, 36, 1991–2011. [CrossRef]
7. Azizi, H.; Nejatian, N. Evaluation of the Climate Change Impact on the Intensity and Return Period for Drought Indices of SPI and SPEI (Study Area: Varamin Plain). Water Supply 2022, 22, 4373–4386. [CrossRef]
8. Banan-Dallalian, M.; Shokatian-Beiragh, M.; Golshani, A.; Abdi, A. Use of a Bayesian Network for Storm-Induced Flood Risk Assessment and Effectiveness of Ecosystem-Based Risk Reduction Measures in Coastal Areas (Port of Sur, Sultanate of Oman). Ocean Eng. 2023, 270, 113662. [CrossRef]
9. Alayi, R.; Mohkam, M.; Seyednouri, S.R.; Ahmadi, M.H.; Sharifpur, M. Energy/Economic Analysis and Optimization of on-Grid Photovoltaic System Using CPSO Algorithm. Sustainability 2021, 13, 12420. [CrossRef]
10. Sedlmeir, J.; Buhl, H.U.; Fridgen, G.; Keller, R. The Energy Consumption of Blockchain Technology: Beyond Myth. Bus. Inf. Syst. Eng. 2020, 62, 599–608. [CrossRef]
11. Aruga, K.; Islam, M.M.; Jannat, A. Effects of COVID-19 on Indian Energy Consumption. Sustainability 2020, 12, 5616. [CrossRef]
12. Ren, S.; Hao, Y.; Xu, L.; Wu, H.; Ba, N. Digitalization and Energy: How Does Internet Development Affect China’s Energy Consumption? Energy Econ. 2021, 98, 105220. [CrossRef]
13. Osobajo, O.A.; Otitoju, A.; Otitoju, M.A.; Oke, A. The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions. Sustainability 2020, 12, 7965. [CrossRef]
14. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building Energy Consumption Prediction for Residential Buildings Using Deep Learning and Other Machine Learning Techniques. J. Build. Eng. 2022, 45, 103406. [CrossRef]
15. Li, X.; Zhou, Y.; Yu, S.; Jia, G.; Li, H.; Li, W. Urban Heat Island Impacts on Building Energy Consumption: A Review of Approaches and Findings. Energy 2019, 174, 407–419. [CrossRef]
16. Robinson, C.; Dilkina, B.; Hubbs, J.; Zhang, W.; Guhathakurta, S.; Brown, M.A.; Pendyala, R.M. Machine Learning Approaches for Estimating Commercial Building Energy Consumption. Appl. Energy 2017, 208, 889–904. [CrossRef]
17. Bourdeau, M.; Zhai, X.Q.; Nefzaoui, E.; Guo, X.; Chatellier, P. Modeling and Forecasting Building Energy Consumption: A Review of Data-Driven Techniques. Sustain. Cities Soc. 2019, 48, 101533. [CrossRef]
18. Yuan, S.; Hu, Z.Z.; Lin, J.R.; Zhang, Y.Y. A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data. Sensors 2021, 21, 1395. [CrossRef]
19. Sharghi, E.; Nourani, V.; Molajou, A.; Najafi, H. Conjunction of Emotional ANN (EANN) and Wavelet Transform for RainfallRunoff Modeling. J. Hydroinform. 2019, 21, 136–152. [CrossRef]
20. Ahmadi, M.H.; Kumar, R.; Assad, M.E.H.; Ngo, P.T.T. Applications of Machine Learning Methods in Modeling Various Types of Heat Pipes: A Review. J. Therm. Anal. Calorim. 2021, 146, 2333–2341. [CrossRef]
21. Mojtahedi, A.; Dadashzadeh, M.; Azizkhani, M.; Mohammadian, A.; Almasi, R. Assessing Climate and Human Activity Effects on Lake Characteristics Using Spatio-Temporal Satellite Data and an Emotional Neural Network. Environ. Earth Sci. 2022, 81, 61. [CrossRef]
22. Liu, Y.; Chen, H.; Zhang, L.; Wu, X.; Wang, X.J. Energy Consumption Prediction and Diagnosis of Public Buildings Based on Support Vector Machine Learning: A Case Study in China. J. Clean. Prod. 2020, 272, 122542. [CrossRef]
23. Walker, S.; Khan, W.; Katic, K.; Maassen, W.; Zeiler, W. Accuracy of Different Machine Learning Algorithms and Added-Value of Predicting Aggregated-Level Energy Performance of Commercial Buildings. Energy Build. 2020, 209, 109705. [CrossRef]
24. Vapnik, V.; Chapelle, O. Bounds on Error Expectation for Support Vector Machines. Neural Comput. 2000, 12, 2013–2036. [CrossRef] [PubMed]
25. Shao, Y.H.; Chen, W.J.; Liu, L.M.; Deng, N.Y. Laplacian Unit-Hyperplane Learning from Positive and Unlabeled Examples. Inf. Sci. 2015, 314, 152–168. [CrossRef]
26. Wang, Y.; Wang, Y.; Song, Y.; Xie, X.; Huang, L.; Pang, W.; Coghill, G.M. An Efficient V-Minimum Absolute Deviation Distribution Regression Machine. IEEE Access 2020, 8, 85533–85551. [CrossRef]
27. Li, Y.; Wang, Y.; Bi, C.; Jiang, X. Revisiting Transductive Support Vector Machines with Margin Distribution Embedding. Knowl.-Based Syst. 2018, 152, 200–214. [CrossRef]
28. Ouaddi, K.; Benadada, Y.; Mhada, F.Z. Ant Colony System for Dynamic Vehicle Routing Problem with Overtime. Int. J. Adv. Comput. Sci. Appl. 2018, 9, 306–315. [CrossRef]
29. Chu, S.C.; Roddick, J.F.; Pan, J.S. Ant Colony System with Communication Strategies. Inf. Sci. 2004, 167, 63–76. [CrossRef]
30. Elsayed, E.K.; Omar, A.H.; Elsayed, K.E. Smart Solution for STSP Semantic Traveling Salesman Problem via Hybrid Ant Colony System with Genetic Algorithm. Int. J. Intell. Eng. Syst. 2020, 13, 476–489. [CrossRef]
31. Gajpal, Y.; Abad, P. An Ant Colony System (ACS) for Vehicle Routing Problem with Simultaneous Delivery and Pickup. Comput. Oper. Res. 2009, 36, 3215–3223. [CrossRef]
32. Liu, X.F.; Zhan, Z.H.; Deng, J.D.; Li, Y.; Gu, T.; Zhang, J. An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing. IEEE Trans. Evol. Comput. 2018, 22, 113–128. [CrossRef]
33. Opoku, E.A.; Ahmed, S.E.; Song, Y.; Nathoo, F.S. Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data. Entropy 2021, 23, 329. [CrossRef]
34. Shen, H.; Tan, H.; Tzempelikos, A. The Effect of Reflective Coatings on Building Surface Temperatures, Indoor Environment and Energy Consumption—An Experimental Study. Energy Build. 2011, 43, 573–580. [CrossRef]
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spelling Atribución 4.0 Internacional (CC BY 4.0)© 2023 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Anupong, WongchaiMuda, IskandarAuda AbdulAmeer, SabahAl-Kharsan, Ibrahim H.Alviz Meza, AnibalCardenas Escorcia, Yulineth2023-08-23T21:27:10Z2023-08-23T21:27:10Z2023-02-08Anupong, W.; Muda, I.; AbdulAmeer, S.A.; Al-Kharsan, I.H.; Alviz-Meza, A.; Cárdenas-Escrocia, Y. Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System. Sustainability 2023, 15, 3118. https://doi.org/10.3390/ su15043118https://hdl.handle.net/11323/1040510.3390/su150431182071-1050Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO2 production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO2 is reduced to negligible levels.14 páginasapplication/pdfengMDPI AGSwitzerlandhttps://www.mdpi.com/2071-1050/15/4/3118Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony systemArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Sustainability1. Molajou, A.; Afshar, A.; Khosravi, M.; Soleimanian, E.; Vahabzadeh, M.; Variani, H.A. A New Paradigm of Water, Food, and Energy Nexus. Environ. Sci. Pollut. Res. 2021, 28, 1–11. [CrossRef] [PubMed]2. Molajou, A.; Pouladi, P.; Afshar, A. Incorporating Social System into Water-Food-Energy Nexus. Water Resour. Manag. 2021, 35, 4561–4580. [CrossRef]3. Ahmadi, M.H.; Sayyaadi, H.; Dehghani, S.; Hosseinzade, H. Designing a Solar Powered Stirling Heat Engine Based on Multiple Criteria: Maximized Thermal Efficiency and Power. Energy Convers. Manag. 2013, 75, 282–291. [CrossRef]4. Al Doury, R.R.J.; Salem, T.K.; Nazzal, I.T.; Kumar, R.; Sadeghzadeh, M. A Novel Developed Method to Study the Energy/Exergy Flows of Buildings Compared to the Traditional Method. J. Therm. Anal. Calorim. 2021, 145, 1151–1161. [CrossRef]5. Ahmadi, M.H.; Jashnani, H.; Chau, K.W.; Kumar, R.; Rosen, M.A. Carbon Dioxide Emissions Prediction of Five Middle Eastern Countries Using Artificial Neural Networks. Energy Sources A Recovery Util. Environ. Eff. 2019, 41, 1–13. [CrossRef]6. Yavari, F.; Salehi Neyshabouri, S.A.; Yazdi, J.; Molajou, A.; Brysiewicz, A. A Novel Framework for Urban Flood Damage Assessment. Water Resour. Manag. 2022, 36, 1991–2011. [CrossRef]7. Azizi, H.; Nejatian, N. Evaluation of the Climate Change Impact on the Intensity and Return Period for Drought Indices of SPI and SPEI (Study Area: Varamin Plain). Water Supply 2022, 22, 4373–4386. [CrossRef]8. Banan-Dallalian, M.; Shokatian-Beiragh, M.; Golshani, A.; Abdi, A. Use of a Bayesian Network for Storm-Induced Flood Risk Assessment and Effectiveness of Ecosystem-Based Risk Reduction Measures in Coastal Areas (Port of Sur, Sultanate of Oman). Ocean Eng. 2023, 270, 113662. [CrossRef]9. Alayi, R.; Mohkam, M.; Seyednouri, S.R.; Ahmadi, M.H.; Sharifpur, M. Energy/Economic Analysis and Optimization of on-Grid Photovoltaic System Using CPSO Algorithm. Sustainability 2021, 13, 12420. [CrossRef]10. Sedlmeir, J.; Buhl, H.U.; Fridgen, G.; Keller, R. The Energy Consumption of Blockchain Technology: Beyond Myth. Bus. Inf. Syst. Eng. 2020, 62, 599–608. [CrossRef]11. Aruga, K.; Islam, M.M.; Jannat, A. Effects of COVID-19 on Indian Energy Consumption. Sustainability 2020, 12, 5616. [CrossRef]12. Ren, S.; Hao, Y.; Xu, L.; Wu, H.; Ba, N. Digitalization and Energy: How Does Internet Development Affect China’s Energy Consumption? Energy Econ. 2021, 98, 105220. [CrossRef]13. Osobajo, O.A.; Otitoju, A.; Otitoju, M.A.; Oke, A. The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions. Sustainability 2020, 12, 7965. [CrossRef]14. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building Energy Consumption Prediction for Residential Buildings Using Deep Learning and Other Machine Learning Techniques. J. Build. Eng. 2022, 45, 103406. [CrossRef]15. Li, X.; Zhou, Y.; Yu, S.; Jia, G.; Li, H.; Li, W. Urban Heat Island Impacts on Building Energy Consumption: A Review of Approaches and Findings. Energy 2019, 174, 407–419. [CrossRef]16. Robinson, C.; Dilkina, B.; Hubbs, J.; Zhang, W.; Guhathakurta, S.; Brown, M.A.; Pendyala, R.M. Machine Learning Approaches for Estimating Commercial Building Energy Consumption. Appl. Energy 2017, 208, 889–904. [CrossRef]17. Bourdeau, M.; Zhai, X.Q.; Nefzaoui, E.; Guo, X.; Chatellier, P. Modeling and Forecasting Building Energy Consumption: A Review of Data-Driven Techniques. Sustain. Cities Soc. 2019, 48, 101533. [CrossRef]18. Yuan, S.; Hu, Z.Z.; Lin, J.R.; Zhang, Y.Y. A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data. Sensors 2021, 21, 1395. [CrossRef]19. Sharghi, E.; Nourani, V.; Molajou, A.; Najafi, H. Conjunction of Emotional ANN (EANN) and Wavelet Transform for RainfallRunoff Modeling. J. Hydroinform. 2019, 21, 136–152. [CrossRef]20. Ahmadi, M.H.; Kumar, R.; Assad, M.E.H.; Ngo, P.T.T. Applications of Machine Learning Methods in Modeling Various Types of Heat Pipes: A Review. J. Therm. Anal. Calorim. 2021, 146, 2333–2341. [CrossRef]21. Mojtahedi, A.; Dadashzadeh, M.; Azizkhani, M.; Mohammadian, A.; Almasi, R. Assessing Climate and Human Activity Effects on Lake Characteristics Using Spatio-Temporal Satellite Data and an Emotional Neural Network. Environ. Earth Sci. 2022, 81, 61. [CrossRef]22. Liu, Y.; Chen, H.; Zhang, L.; Wu, X.; Wang, X.J. Energy Consumption Prediction and Diagnosis of Public Buildings Based on Support Vector Machine Learning: A Case Study in China. J. Clean. Prod. 2020, 272, 122542. [CrossRef]23. Walker, S.; Khan, W.; Katic, K.; Maassen, W.; Zeiler, W. Accuracy of Different Machine Learning Algorithms and Added-Value of Predicting Aggregated-Level Energy Performance of Commercial Buildings. Energy Build. 2020, 209, 109705. [CrossRef]24. Vapnik, V.; Chapelle, O. Bounds on Error Expectation for Support Vector Machines. Neural Comput. 2000, 12, 2013–2036. [CrossRef] [PubMed]25. Shao, Y.H.; Chen, W.J.; Liu, L.M.; Deng, N.Y. Laplacian Unit-Hyperplane Learning from Positive and Unlabeled Examples. Inf. Sci. 2015, 314, 152–168. [CrossRef]26. Wang, Y.; Wang, Y.; Song, Y.; Xie, X.; Huang, L.; Pang, W.; Coghill, G.M. An Efficient V-Minimum Absolute Deviation Distribution Regression Machine. IEEE Access 2020, 8, 85533–85551. [CrossRef]27. Li, Y.; Wang, Y.; Bi, C.; Jiang, X. Revisiting Transductive Support Vector Machines with Margin Distribution Embedding. Knowl.-Based Syst. 2018, 152, 200–214. [CrossRef]28. Ouaddi, K.; Benadada, Y.; Mhada, F.Z. Ant Colony System for Dynamic Vehicle Routing Problem with Overtime. Int. J. Adv. Comput. Sci. Appl. 2018, 9, 306–315. [CrossRef]29. Chu, S.C.; Roddick, J.F.; Pan, J.S. Ant Colony System with Communication Strategies. Inf. Sci. 2004, 167, 63–76. [CrossRef]30. Elsayed, E.K.; Omar, A.H.; Elsayed, K.E. Smart Solution for STSP Semantic Traveling Salesman Problem via Hybrid Ant Colony System with Genetic Algorithm. Int. J. Intell. Eng. Syst. 2020, 13, 476–489. [CrossRef]31. Gajpal, Y.; Abad, P. An Ant Colony System (ACS) for Vehicle Routing Problem with Simultaneous Delivery and Pickup. Comput. Oper. Res. 2009, 36, 3215–3223. [CrossRef]32. Liu, X.F.; Zhan, Z.H.; Deng, J.D.; Li, Y.; Gu, T.; Zhang, J. An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing. IEEE Trans. Evol. Comput. 2018, 22, 113–128. [CrossRef]33. Opoku, E.A.; Ahmed, S.E.; Song, Y.; Nathoo, F.S. Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data. Entropy 2021, 23, 329. [CrossRef]34. Shen, H.; Tan, H.; Tzempelikos, A. The Effect of Reflective Coatings on Building Surface Temperatures, Indoor Environment and Energy Consumption—An Experimental Study. Energy Build. 2011, 43, 573–580. [CrossRef]141415BuildingEnergy optimizationACSSVMPublicationORIGINALEnergy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System.pdfEnergy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System.pdfArtículoapplication/pdf2947315https://repositorio.cuc.edu.co/bitstreams/ee096842-b518-49b7-abec-bbedcf17a756/download50faaccc885e9be3c138128855945360MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/a786b89e-42dc-4e24-91e7-a7fbc57d7ea1/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTEnergy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System.pdf.txtEnergy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System.pdf.txtExtracted texttext/plain56684https://repositorio.cuc.edu.co/bitstreams/31a54507-2a1a-4696-a29c-67d0144b1e16/download53d4d5ddbf2f45bf118c49780b5ff3d0MD53THUMBNAILEnergy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System.pdf.jpgEnergy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System.pdf.jpgGenerated Thumbnailimage/jpeg15947https://repositorio.cuc.edu.co/bitstreams/dc68625c-47dc-4d60-b9e2-deef575ecb1e/download4c73be789d0f7eb12f1b14ca204a3c77MD5411323/10405oai:repositorio.cuc.edu.co:11323/104052024-09-17 14:10:59.107https://creativecommons.org/licenses/by/4.0/© 2023 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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