Forecast of the demand for hourly electric energy by artificial neural networks
Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation...
- Autores:
-
Viloria, Amelec
RONCALLO PICHON, ALBERTO DE JESUS
Hernandez-P, Hugo
REDONDO BILBAO, OSMAN ENRIQUE
Pineda, Omar
Vargas, Jesús
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7772
- Acceso en línea:
- https://hdl.handle.net/11323/7772
https://doi.org/10.1007/978-981-15-3125-5_46
https://repositorio.cuc.edu.co/
- Palabra clave:
- Forecasting
Electric load
Artificial neural networks
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Forecast of the demand for hourly electric energy by artificial neural networks |
| title |
Forecast of the demand for hourly electric energy by artificial neural networks |
| spellingShingle |
Forecast of the demand for hourly electric energy by artificial neural networks Forecasting Electric load Artificial neural networks |
| title_short |
Forecast of the demand for hourly electric energy by artificial neural networks |
| title_full |
Forecast of the demand for hourly electric energy by artificial neural networks |
| title_fullStr |
Forecast of the demand for hourly electric energy by artificial neural networks |
| title_full_unstemmed |
Forecast of the demand for hourly electric energy by artificial neural networks |
| title_sort |
Forecast of the demand for hourly electric energy by artificial neural networks |
| dc.creator.fl_str_mv |
Viloria, Amelec RONCALLO PICHON, ALBERTO DE JESUS Hernandez-P, Hugo REDONDO BILBAO, OSMAN ENRIQUE Pineda, Omar Vargas, Jesús |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec RONCALLO PICHON, ALBERTO DE JESUS Hernandez-P, Hugo REDONDO BILBAO, OSMAN ENRIQUE Pineda, Omar Vargas, Jesús |
| dc.subject.spa.fl_str_mv |
Forecasting Electric load Artificial neural networks |
| topic |
Forecasting Electric load Artificial neural networks |
| description |
Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation resources, which means a reduction in costs. In the long term, it constitutes a main indicator for the generation of investment signals for future installed capacity. This research proposes a prognostic model for the demand of electrical energy in Bogota, Colombia at hourly level in a full week, through Artificial Neural Network. |
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2020 |
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2020 |
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2021-01-27T15:03:09Z |
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2021-01-27T15:03:09Z |
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Artículo de revista |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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1. Perez R et al (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham 2. Silva V, Jesús A (2013) Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced materials research, vol 601. Trans Tech Publications, Switzerland, pp 618–625 3. Perez R, Inga E, Aguila A, Vásquez C, Lima L, Viloria A, Henry MA (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International conference on sensing and imaging, June. Springer, Cham, pp 174–185 4. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 36:1627–1637 (Preprint) 5. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN: 8420540250 6. Kulkarni S, Haidar I (2009) Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int J Comput Sci Inf Secur 2(1):81–89 7. Mazón JN, Trujillo J, Serrano M, Piattini M (2005) Designing data warehouses: from business requirement analysis to multidimensional modeling. In: Proceedings of the 1st international workshop on requirements engineering for business need and IT alignment, Paris, France 8. Ben Salem S, Naouali S, Chtourou Z (2018) A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput Electron Eng 68:463–483. 9. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted Gaussian algorithm means. Stat Probab Lett 137:148–156. 10. Abhay KA, Badal NA (2015) Novel approach for intelligent distribution of data warehouses. Egypt Inform J 17(1):147–159 11. Abdul Masud M, Zhexue Huang J, Wei C, Wang J, Khan I, Zhong M (2018) I-nice: a new approach for identifying the number of clusters and initial cluster centres. Inf Sci. 12. Rahman MA, Islam MZ, Bossomaier T (2015) ModEx and Seed-Detective: two novel techniques for high quality clustering by using good initial seeds in K-means. J King Saud Univ Comput Inf Sci 27:113–128. 13. Maren AJ, Harston CT, Pap RM (2014) Handbook of neural computing applications. Academic Press, San Diego 14. Baughman DR, Liu YA (2014) Neural networks in bioprocessing and chemical engineering. Academic Press, San Diego 15. Fast M, Assadi M, De S (2009) Development and multi-utility of an ANN model for an industrial gas turbine. Appl Energy 86(1):9–17 16. Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2016) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28:2005–2016 17. Tüfekci P (2014) Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int J Electr Power Energy Syst 60:126–140 18. Samanta S, Acharjee S, Mukherjee A, Das D, Dey N (2013) Ant weight lifting algorithm for image segmentation. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–5 19. Jagatheesan K, Anand B, Dey N, Ashour AS (2015) Artificial intelligence in performance analysis of load frequency control in thermal-wind-hydro power systems. Artif Intell 6(7) 20. Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5:373–401 21. Laha P, Chakraborty B (2017) Energy model—a tool for preventing energy dysfunction. Renew Sustain Energy Rev 73:95–114 22. Moldes O, Mejuto J, Rial-Otero R, Simal-Gandara J (2017) A critical review on the applications of artificial neural networks in winemaking technology. Crit Rev Food Sci Nutr 57:2896–2908 |
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Viloria, AmelecRONCALLO PICHON, ALBERTO DE JESUSHernandez-P, HugoREDONDO BILBAO, OSMAN ENRIQUEPineda, OmarVargas, Jesús2021-01-27T15:03:09Z2021-01-27T15:03:09Z2020https://hdl.handle.net/11323/7772https://doi.org/10.1007/978-981-15-3125-5_46Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation resources, which means a reduction in costs. In the long term, it constitutes a main indicator for the generation of investment signals for future installed capacity. This research proposes a prognostic model for the demand of electrical energy in Bogota, Colombia at hourly level in a full week, through Artificial Neural Network.Viloria, AmelecRONCALLO PICHON, ALBERTO DE JESUS-will be generated-orcid-0000-0002-1290-0132-600Hernandez-P, HugoREDONDO BILBAO, OSMAN ENRIQUE-will be generated-orcid-0000-0002-5477-0655-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Vargas, Jesúsapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Electrical Engineeringhttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_46ForecastingElectric loadArtificial neural networksForecast of the demand for hourly electric energy by artificial neural networksArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Perez R et al (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham2. Silva V, Jesús A (2013) Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced materials research, vol 601. Trans Tech Publications, Switzerland, pp 618–6253. Perez R, Inga E, Aguila A, Vásquez C, Lima L, Viloria A, Henry MA (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International conference on sensing and imaging, June. Springer, Cham, pp 174–1854. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 36:1627–1637 (Preprint)5. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN: 84205402506. Kulkarni S, Haidar I (2009) Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int J Comput Sci Inf Secur 2(1):81–897. Mazón JN, Trujillo J, Serrano M, Piattini M (2005) Designing data warehouses: from business requirement analysis to multidimensional modeling. In: Proceedings of the 1st international workshop on requirements engineering for business need and IT alignment, Paris, France8. Ben Salem S, Naouali S, Chtourou Z (2018) A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput Electron Eng 68:463–483.9. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted Gaussian algorithm means. Stat Probab Lett 137:148–156.10. Abhay KA, Badal NA (2015) Novel approach for intelligent distribution of data warehouses. Egypt Inform J 17(1):147–15911. Abdul Masud M, Zhexue Huang J, Wei C, Wang J, Khan I, Zhong M (2018) I-nice: a new approach for identifying the number of clusters and initial cluster centres. Inf Sci.12. Rahman MA, Islam MZ, Bossomaier T (2015) ModEx and Seed-Detective: two novel techniques for high quality clustering by using good initial seeds in K-means. J King Saud Univ Comput Inf Sci 27:113–128.13. Maren AJ, Harston CT, Pap RM (2014) Handbook of neural computing applications. Academic Press, San Diego14. Baughman DR, Liu YA (2014) Neural networks in bioprocessing and chemical engineering. Academic Press, San Diego15. Fast M, Assadi M, De S (2009) Development and multi-utility of an ANN model for an industrial gas turbine. Appl Energy 86(1):9–1716. Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2016) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 28:2005–201617. Tüfekci P (2014) Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int J Electr Power Energy Syst 60:126–14018. Samanta S, Acharjee S, Mukherjee A, Das D, Dey N (2013) Ant weight lifting algorithm for image segmentation. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–519. Jagatheesan K, Anand B, Dey N, Ashour AS (2015) Artificial intelligence in performance analysis of load frequency control in thermal-wind-hydro power systems. Artif Intell 6(7)20. Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5:373–40121. Laha P, Chakraborty B (2017) Energy model—a tool for preventing energy dysfunction. Renew Sustain Energy Rev 73:95–11422. Moldes O, Mejuto J, Rial-Otero R, Simal-Gandara J (2017) A critical review on the applications of artificial neural networks in winemaking technology. 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