Predicting short-term electricity demand through artificial neural network
Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the fore...
- Autores:
-
Viloria, Amelec
García Guliany, Jesús
Varela Izquierdo, Noel
Pineda, Omar
Hernández Palma, Hugo
Valero, Lesbia
Marín-González, Freddy
- 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:11323/7752
- Acceso en línea:
- https://hdl.handle.net/11323/7752
https://doi.org/10.1007/978-981-15-2612-1_14
https://repositorio.cuc.edu.co/
- Palabra clave:
- Primary feeder
Demand short-term electricity prognosis
Neural networks
Forecast accuracy
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Predicting short-term electricity demand through artificial neural network |
| title |
Predicting short-term electricity demand through artificial neural network |
| spellingShingle |
Predicting short-term electricity demand through artificial neural network Primary feeder Demand short-term electricity prognosis Neural networks Forecast accuracy |
| title_short |
Predicting short-term electricity demand through artificial neural network |
| title_full |
Predicting short-term electricity demand through artificial neural network |
| title_fullStr |
Predicting short-term electricity demand through artificial neural network |
| title_full_unstemmed |
Predicting short-term electricity demand through artificial neural network |
| title_sort |
Predicting short-term electricity demand through artificial neural network |
| dc.creator.fl_str_mv |
Viloria, Amelec García Guliany, Jesús Varela Izquierdo, Noel Pineda, Omar Hernández Palma, Hugo Valero, Lesbia Marín-González, Freddy |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec García Guliany, Jesús Varela Izquierdo, Noel Pineda, Omar Hernández Palma, Hugo Valero, Lesbia Marín-González, Freddy |
| dc.subject.spa.fl_str_mv |
Primary feeder Demand short-term electricity prognosis Neural networks Forecast accuracy |
| topic |
Primary feeder Demand short-term electricity prognosis Neural networks Forecast accuracy |
| description |
Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems. |
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2020 |
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2020 |
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2021-01-22T23:38:47Z |
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2021-01-22T23:38:47Z |
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Artículo de revista |
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https://doi.org/10.1007/978-981-15-2612-1_14 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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1. Hu, C., Du, S., Su, J., et al.: Discussion on the ways of purchasing and selling electricity and the mode of operation in China’s electricity sales companies under the background of new electric power reform. Power Netw. Technol. 40(11), 3293–3299 (2016) 2. Xue, Y., Lai, Y.: The integration of great energy thinking and big data thinking: Big data and electricity big data. Power Syst. Autom. 40(1), 1–8 (2016) 3. Wang, Y., Chen, Q., Kang, C., et al.: Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans. Smart Grid 7(5), 2437–2447 (2017) 4. Rong, L., Guosheng, F., Weidai, D.: Statistical Analysis and Application of SAS (China Machine Press, 2011) 5. Sanchez, L., Vásquez, C., Viloria, A., Meza-Estrada, C.: Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018) 6. Sánchez, L., Vásquez, C., Viloria, A., Rodríguez Potes, L.: Greenhouse gases emissions and electric power generation in Latin American countries in the period 2006–2013. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018) 7. Perez, R., et al.: 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 (2018) 8. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. Preprint 1–11 (2019) 9. Ghia, A., Rosso, A.: Análisis de respuesta de la demanda para mejorar la eficiencia de sistemas eléctricos, 2nd edn. Camara Argetina de la Construccion, Buenos Aires (2009) 10. Pérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad vol. I, no. I (2005) 11. Silva, V., Jesús, A.: Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced Materials Research, vol. 601, pp. 618–625. Trans Tech Publications (2013) 12. Perez, R., Inga, E., Aguila, A., Vásquez, C., Lima, L., Viloria, A., Henry, M.A.: Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International Conference on Sensing and Imaging, pp. 174–185). Springer, Cham (2018) 13. Ozger, M., Cetinkaya, O., Akan, O.B.: Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob. Netw. Appl. 23(4), 956–966 (2017) 14. Bradley, P., Fayyad, U., Mangasarian, O.: Mathematical programming for data mining: formulations and challenges. Informs J. Comput. 11, 217–238 (1999) 15. Rahmani, A.M., Liljeberg, P., Preden, J., Jantsch, A.: Fog Computing in the Internet of Things. Springer, New York (2018). ISBN 978-3-319-57638-1, ISBN 978-3-319-57639-8 (eBook) 16. Abualigah, L.M., Khader, A.T., Al-Beta, M.A., Alomari, O.A.: Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst. Appl. 84, 24–36 (2017) |
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Viloria, AmelecGarcía Guliany, JesúsVarela Izquierdo, NoelPineda, OmarHernández Palma, HugoValero, LesbiaMarín-González, Freddy2021-01-22T23:38:47Z2021-01-22T23:38:47Z2020https://hdl.handle.net/11323/7752https://doi.org/10.1007/978-981-15-2612-1_14Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems.Viloria, AmelecGarcía Guliany, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Hernández Palma, HugoValero, LesbiaMarín González, Freddy-will be generated-orcid-0000-0002-3935-8806-600application/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/10.1007/978-981-15-2612-1_14Primary feederDemand short-term electricity prognosisNeural networksForecast accuracyPredicting short-term electricity demand through artificial neural networkArtículo 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. Hu, C., Du, S., Su, J., et al.: Discussion on the ways of purchasing and selling electricity and the mode of operation in China’s electricity sales companies under the background of new electric power reform. Power Netw. Technol. 40(11), 3293–3299 (2016)2. Xue, Y., Lai, Y.: The integration of great energy thinking and big data thinking: Big data and electricity big data. Power Syst. Autom. 40(1), 1–8 (2016)3. Wang, Y., Chen, Q., Kang, C., et al.: Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans. Smart Grid 7(5), 2437–2447 (2017)4. Rong, L., Guosheng, F., Weidai, D.: Statistical Analysis and Application of SAS (China Machine Press, 2011)5. Sanchez, L., Vásquez, C., Viloria, A., Meza-Estrada, C.: Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)6. Sánchez, L., Vásquez, C., Viloria, A., Rodríguez Potes, L.: Greenhouse gases emissions and electric power generation in Latin American countries in the period 2006–2013. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)7. Perez, R., et al.: 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 (2018)8. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. Preprint 1–11 (2019)9. Ghia, A., Rosso, A.: Análisis de respuesta de la demanda para mejorar la eficiencia de sistemas eléctricos, 2nd edn. Camara Argetina de la Construccion, Buenos Aires (2009)10. Pérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad vol. I, no. I (2005)11. Silva, V., Jesús, A.: Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced Materials Research, vol. 601, pp. 618–625. Trans Tech Publications (2013)12. Perez, R., Inga, E., Aguila, A., Vásquez, C., Lima, L., Viloria, A., Henry, M.A.: Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International Conference on Sensing and Imaging, pp. 174–185). Springer, Cham (2018)13. Ozger, M., Cetinkaya, O., Akan, O.B.: Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob. Netw. Appl. 23(4), 956–966 (2017)14. Bradley, P., Fayyad, U., Mangasarian, O.: Mathematical programming for data mining: formulations and challenges. Informs J. Comput. 11, 217–238 (1999)15. Rahmani, A.M., Liljeberg, P., Preden, J., Jantsch, A.: Fog Computing in the Internet of Things. Springer, New York (2018). ISBN 978-3-319-57638-1, ISBN 978-3-319-57639-8 (eBook)16. Abualigah, L.M., Khader, A.T., Al-Beta, M.A., Alomari, O.A.: Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst. Appl. 84, 24–36 (2017)PublicationORIGINALPredicting short-term electricity demand through artificial neural network.pdfPredicting short-term electricity demand through artificial neural network.pdfapplication/pdf93901https://repositorio.cuc.edu.co/bitstreams/d1ed41ee-4f51-4b6a-ac22-d25f439f2997/download54a1e158c20793288a210325903ddcbaMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/c2172076-08c0-4cfb-914f-b56ef4d28cad/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/8347b120-ca46-40fc-b151-355dd92dbd4b/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILPredicting short-term electricity demand through artificial neural network.pdf.jpgPredicting short-term electricity demand through artificial neural 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