Forecast of operational data in electric energy plants using adaptive algorithm
Traditional time series methods offer models whose parameters remain constant over time. However, industrial supply and demand processes require timely decisions based on a dynamic reality. A change in configuration, turning off, or on a production line or process, modifies the problem and the varia...
- Autores:
-
Viloria, Amelec
García Guiliany, Jesús Enrique
Hernandez-P, Hugo
CABAS VASQUEZ, LUIS CARLOS
Pineda, Omar
- 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:11323/7726
- Acceso en línea:
- https://hdl.handle.net/11323/7726
https://doi.org/10.1007/978-981-15-3125-5_48
https://repositorio.cuc.edu.co/
- Palabra clave:
- Time series models
Estimation
Forecasts
Data analysis
Data mining
Statistical learning
Decision trees
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Forecast of operational data in electric energy plants using adaptive algorithm |
| title |
Forecast of operational data in electric energy plants using adaptive algorithm |
| spellingShingle |
Forecast of operational data in electric energy plants using adaptive algorithm Time series models Estimation Forecasts Data analysis Data mining Statistical learning Decision trees |
| title_short |
Forecast of operational data in electric energy plants using adaptive algorithm |
| title_full |
Forecast of operational data in electric energy plants using adaptive algorithm |
| title_fullStr |
Forecast of operational data in electric energy plants using adaptive algorithm |
| title_full_unstemmed |
Forecast of operational data in electric energy plants using adaptive algorithm |
| title_sort |
Forecast of operational data in electric energy plants using adaptive algorithm |
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Viloria, Amelec García Guiliany, Jesús Enrique Hernandez-P, Hugo CABAS VASQUEZ, LUIS CARLOS Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec García Guiliany, Jesús Enrique Hernandez-P, Hugo CABAS VASQUEZ, LUIS CARLOS Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Time series models Estimation Forecasts Data analysis Data mining Statistical learning Decision trees |
| topic |
Time series models Estimation Forecasts Data analysis Data mining Statistical learning Decision trees |
| description |
Traditional time series methods offer models whose parameters remain constant over time. However, industrial supply and demand processes require timely decisions based on a dynamic reality. A change in configuration, turning off, or on a production line or process, modifies the problem and the variables to be predicted. Decision support systems must dynamically adapt in order to respond quickly and appropriately to operations and their processes. This methodology is based on obtaining, for each period, the model that best fits the data, evaluating many alternatives and using statistical learning techniques. In this way, the model will adapt to the data in practice and make decisions based on experience. With three months of testing for the estimation of variables associated with supply and demand processes, predictions that differ less than 8 hundredths (less than 0.08) or 0.1% of the measured value were obtained. This indicates that data science and statistical learning represent an important area of research for variable prediction and process optimization. |
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2020 |
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2020 |
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2021-01-20T18:36:18Z |
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2021-01-20T18:36:18Z |
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Artículo de revista |
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https://doi.org/10.1007/978-981-15-3125-5_48 |
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Corporación Universidad de la Costa |
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eng |
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eng |
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1. Sanchez L, Vásquez C, Viloria A, Cmeza-Estrada (2018) 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, Berlin 2. 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. Springer, Berlin, pp 174–185 3. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 1–11 4. 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. 5. Bontempi G, Ben Taieb S, Borgne YA (2013) Machine learning strategies for time series forecasting. In: Aufaure MA, Zimányi E (eds) Lecture notes in business information processing, vol 138, no 1. Springer, Heidelberg, pp 70–73 6. Abdul Masud M, Zhexue Huang J, Wei C, Wang J, Khan I, Zhong M (2018) Inice: a new approach for identifying the number of clusters and initial cluster centres. Inf Sci. 7. Sánchez L, Vásquez C, Viloria A, Rodríguez Potes L (2018) 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, Berlin 8. Sun M, Konstantelos I, Strbac G (2017) C-vine copula mixture model for clustering of residential electrical load pattern data. Power Syst IEEE Trans On 32(3):2382–2393 9. 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, Berlin 10. 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, pp 618–625 11. Kim M, Park S, Han K, Kim N, Kyun Choi J (2018) Dynamics of electricity consumers for classifying power consumption data using PCA.In: 2018 IEEE international conference on big data and smart computing (BigComp), pp 697–700 12. Chen S, Liu CC (2016) From demand response to transactive energy: state of the art. J Mod Power Syst Clean Energy 13. Sun M, Teng F, Konstantelos I, Strbac G (2018) An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources. Energy 14. Capizzi G, Sciuto GL, Napoli C, Tramontana E (2017) An advanced neural network based solution to enforce dispatch continuity in smart grids. Appl Soft Comput 15. Melzi F, Same A, Zayani M, Oukhellou L (2017) A dedicated mixture model for clustering smart meter data: identification and analysis of electricity consumption behaviors. Energies 10:1446 16. Chen T, Alsafasfeh Q, Pourbabak H, Su W (2017) The next-generation US retail electricity market with customers and prosumers—a bibliographical survey. Energies 11:8 |
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Viloria, AmelecGarcía Guiliany, Jesús EnriqueHernandez-P, HugoCABAS VASQUEZ, LUIS CARLOSPineda, Omar2021-01-20T18:36:18Z2021-01-20T18:36:18Z2020https://hdl.handle.net/11323/7726https://doi.org/10.1007/978-981-15-3125-5_48Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Traditional time series methods offer models whose parameters remain constant over time. However, industrial supply and demand processes require timely decisions based on a dynamic reality. A change in configuration, turning off, or on a production line or process, modifies the problem and the variables to be predicted. Decision support systems must dynamically adapt in order to respond quickly and appropriately to operations and their processes. This methodology is based on obtaining, for each period, the model that best fits the data, evaluating many alternatives and using statistical learning techniques. In this way, the model will adapt to the data in practice and make decisions based on experience. With three months of testing for the estimation of variables associated with supply and demand processes, predictions that differ less than 8 hundredths (less than 0.08) or 0.1% of the measured value were obtained. This indicates that data science and statistical learning represent an important area of research for variable prediction and process optimization.Viloria, AmelecGarcía Guiliany, Jesús Enrique-will be generated-orcid-0000-0003-3777-3667-600Hernandez-P, HugoCABAS VASQUEZ, LUIS CARLOS-will be generated-orcid-0000-0003-0524-7945-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/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/10.1007/978-981-15-3125-5_48Time series modelsEstimationForecastsData analysisData miningStatistical learningDecision treesForecast of operational data in electric energy plants using adaptive algorithmArtículo 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. Sanchez L, Vásquez C, Viloria A, Cmeza-Estrada (2018) 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, Berlin2. 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. Springer, Berlin, pp 174–1853. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 1–114. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted Gaussian algorithm means. Stat Probab Lett 137:148–156.5. Bontempi G, Ben Taieb S, Borgne YA (2013) Machine learning strategies for time series forecasting. In: Aufaure MA, Zimányi E (eds) Lecture notes in business information processing, vol 138, no 1. Springer, Heidelberg, pp 70–736. Abdul Masud M, Zhexue Huang J, Wei C, Wang J, Khan I, Zhong M (2018) Inice: a new approach for identifying the number of clusters and initial cluster centres. Inf Sci.7. Sánchez L, Vásquez C, Viloria A, Rodríguez Potes L (2018) 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, Berlin8. Sun M, Konstantelos I, Strbac G (2017) C-vine copula mixture model for clustering of residential electrical load pattern data. Power Syst IEEE Trans On 32(3):2382–23939. 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, Berlin10. 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, pp 618–62511. Kim M, Park S, Han K, Kim N, Kyun Choi J (2018) Dynamics of electricity consumers for classifying power consumption data using PCA.In: 2018 IEEE international conference on big data and smart computing (BigComp), pp 697–70012. Chen S, Liu CC (2016) From demand response to transactive energy: state of the art. J Mod Power Syst Clean Energy13. Sun M, Teng F, Konstantelos I, Strbac G (2018) An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources. Energy14. Capizzi G, Sciuto GL, Napoli C, Tramontana E (2017) An advanced neural network based solution to enforce dispatch continuity in smart grids. Appl Soft Comput15. Melzi F, Same A, Zayani M, Oukhellou L (2017) A dedicated mixture model for clustering smart meter data: identification and analysis of electricity consumption behaviors. Energies 10:144616. Chen T, Alsafasfeh Q, Pourbabak H, Su W (2017) The next-generation US retail electricity market with customers and prosumers—a bibliographical survey. 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