Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have...

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Autores:
Mariano-Hernández, Deyslen
Hernández Callejo, Luis
Solís, Martín
Zorita Lamadrid, Angel Luis
Duque-Perez, Oscar
Gonzalez Morales, Luis Gerardo
Santos Garcia, Felix
Jaramillo Duque, Álvaro
Ospino C., Adalberto
Alonso Gómez, Víctor
Bello, Hugo J.
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9472
Acceso en línea:
https://hdl.handle.net/11323/9472
https://doi.org/10.3390/su14105857
https://repositorio.cuc.edu.co/
Palabra clave:
Drift detection
Electrical consumption forecasting
Energy forecasting
Machine learning
Smart buildings
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_03d9dc46829ac19a4d83a568e5421c72
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9472
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
title Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
spellingShingle Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
Drift detection
Electrical consumption forecasting
Energy forecasting
Machine learning
Smart buildings
title_short Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
title_full Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
title_fullStr Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
title_full_unstemmed Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
title_sort Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildings
dc.creator.fl_str_mv Mariano-Hernández, Deyslen
Hernández Callejo, Luis
Solís, Martín
Zorita Lamadrid, Angel Luis
Duque-Perez, Oscar
Gonzalez Morales, Luis Gerardo
Santos Garcia, Felix
Jaramillo Duque, Álvaro
Ospino C., Adalberto
Alonso Gómez, Víctor
Bello, Hugo J.
dc.contributor.author.spa.fl_str_mv Mariano-Hernández, Deyslen
Hernández Callejo, Luis
Solís, Martín
Zorita Lamadrid, Angel Luis
Duque-Perez, Oscar
Gonzalez Morales, Luis Gerardo
Santos Garcia, Felix
Jaramillo Duque, Álvaro
Ospino C., Adalberto
Alonso Gómez, Víctor
Bello, Hugo J.
dc.subject.proposal.eng.fl_str_mv Drift detection
Electrical consumption forecasting
Energy forecasting
Machine learning
Smart buildings
topic Drift detection
Electrical consumption forecasting
Energy forecasting
Machine learning
Smart buildings
description Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-08-24T14:53:53Z
dc.date.available.none.fl_str_mv 2022-08-24T14:53:53Z
dc.date.issued.none.fl_str_mv 2022-05-12
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.spa.fl_str_mv Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9472
dc.identifier.url.spa.fl_str_mv https://doi.org/10.3390/su14105857
dc.identifier.doi.spa.fl_str_mv 10.3390/su14105857
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 Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857
10.3390/su14105857
2071-1050
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9472
https://doi.org/10.3390/su14105857
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Sustainability
dc.relation.references.spa.fl_str_mv 1. IEA Tracking Buildings. 2021. Available online: https://www.iea.org/reports/tracking-buildings-2021 (accessed on 16 March 2022).
2. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Guz, Ł.; Bocian, M.; Balaras, C.A. An easy and widelapplicable forecast control for heating systems in existing and new buildings: First field experiences. J. Clean. Prod. 2022, 352, 131605. [CrossRef]
3. Devagiri, V.M.; Boeva, V.; Abghari, S.; Basiri, F.; Lavesson, N. Multi-view data analysis techniques for monitoring smart building systems. Sensors 2021, 21, 6775. [CrossRef] [PubMed]
4. Izidio, D.M.; de Mattos Neto, P.S.; Barbosa, L.; de Oliveira, J.F.; Marinho, M.H.D.N.; Rissi, G.F. Evolutionary hybrid system for energy consumption forecasting for smart meters. Energies 2021, 14, 1794. [CrossRef]
5. Hong, T.; Wang, Z.; Luo, X.; Zhang, W. State-of-the-art on research and applications of machine learning in the building life cycle. Energy Build. 2020, 212, 109831. [CrossRef]
6. Kim, J.Y.; Cho, S.B. Electric energy consumption prediction by deep learning with state explainable autoencoder. Energies 2019, 12, 739. [CrossRef]
7. Zeng, A.; Ho, H.; Yu, Y. Prediction of building electricity usage using Gaussian Process Regression. J. Build. Eng. 2020, 28, 101054. [CrossRef]
8. Xu, W.; Peng, H.; Zeng, X.; Zhou, F.; Tian, X.; Peng, X. A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl. Intell. 2019, 49, 3002–3015. [CrossRef]
9. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Anasiewicz, R.; Balaras, C.A. A simple building energy model in form of an equivalent outdoor temperature. Energy Build. 2021, 236, 110766. [CrossRef]
10. Žliobaite, I.; Pechenizkiy, M.; Gama, J. ˙ An Overview of Concept Drift Applications BT—Big Data Analysis: New Algorithms for a New Society; Japkowicz, N., Stefanowski, J., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 91–114, ISBN 978-3-319-26989-4.
11. Iwashita, A.S.; Papa, J.P. An Overview on Concept Drift Learning. IEEE Access 2019, 7, 1532–1547. [CrossRef]
12. Baier, L.; Kühl, N.; Satzger, G.; Hofmann, M.; Mohr, M. Handling concept drifts in regression problems—the error intersection approach. In WI2020 Zentrale Tracks; GITO Verlag: Berlin, Germany, 2020; pp. 210–224.
13. Kahraman, A.; Kantardzic, M.; Kahraman, M.; Kotan, M. A data-driven multi-regime approach for predicting energy consumption. Energies 2021, 14, 6763. [CrossRef]
14. Webb, G.I.; Lee, L.K.; Goethals, B.; Petitjean, F. Analyzing concept drift and shift from sample data. Data Min. Knowl. Discov. 2018, 32, 1179–1199. [CrossRef]
15. Lu, J.; Liu, A.; Dong, F.; Gu, F.; Gama, J.; Zhang, G. Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 2019, 31, 2346–2363. [CrossRef]
16. Brzezinski, D.; Stefanowski, J. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25, 81–94. [CrossRef]
17. Wadewale, K.; Desai, S.; Tennant, M.; Stahl, F.; Rana, O.; Gomes, J.B.; Thakre, A.A.; Redes, E.M.; Padmalatha, E.; Rani, P.; et al. Survey on Method of Drift Detection and Classification for time varying data set. Comput. Biol. Med. 2016, 32, 1–7.
18. Khezri, S.; Tanha, J.; Ahmadi, A.; Sharifi, A. A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams. Neurocomputing 2021, 442, 125–145. [CrossRef]
19. Fekri, M.N.; Patel, H.; Grolinger, K.; Sharma, V. Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network. Appl. Energy 2021, 282, 116177. [CrossRef]
20. Jagait, R.K.; Fekri, M.N.; Grolinger, K.; Mir, S. Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA. IEEE Access 2021, 9, 98992–99008. [CrossRef]
21. Fenza, G.; Gallo, M.; Loia, V. Drift-aware methodology for anomaly detection in smart grid. IEEE Access 2019, 7, 9645–9657. [CrossRef]
22. Mehmood, H.; Kostakos, P.; Cortes, M.; Anagnostopoulos, T.; Pirttikangas, S.; Gilman, E. Concept drift adaptation techniques in distributed environment for real-world data streams. Smart Cities 2021, 4, 349–371. [CrossRef]
23. Ceci, M.; Corizzo, R.; Japkowicz, N.; Mignone, P.; Pio, G. ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids. IEEE Access 2020, 8, 156053–156066. [CrossRef]
24. Yang, Z.; Al-Dahidi, S.; Baraldi, P.; Zio, E.; Montelatici, L. A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments. IEEE Trans. Neural Netw. Learn. Syst. 2020, 31, 309–320. [CrossRef] [PubMed]
25. Silva, R.P.; Zarpelão, B.B.; Cano, A.; Barbon Junior, S. Time series segmentation based on stationarity analysis to improve new samples prediction. Sensors 2021, 21, 7333. [CrossRef] [PubMed]
26. Heusinger, M.; Raab, C.; Schleif, F.M. Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl. 2022, 34, 89–100. [CrossRef]
27. Raab, C.; Heusinger, M.; Schleif, F.M. Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing 2020, 416, 340–351. [CrossRef]
28. Togbe, M.U.; Chabchoub, Y.; Boly, A.; Barry, M.; Chiky, R.; Bahri, M. Anomalies detection using isolation in concept-drifting data streams. Computers 2021, 10, 13. [CrossRef]
29. Moon, J.; Park, S.; Rho, S.; Hwang, E. A comparative analysis of artificial neural network architectures for building energy consumption forecasting. Int. J. Distrib. Sens. Netw. 2019, 15, 155014771987761. [CrossRef]
30. Kiprijanovska, I.; Stankoski, S.; Ilievski, I.; Jovanovski, S.; Gams, M.; Gjoreski, H. HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning. Energies 2020, 13, 2672. [CrossRef]
31. Zor, K.; Çelik, Ö.; Timur, O.; Teke, A. Short-term building electrical energy consumption forecasting by employing gene expression programming and GMDH networks. Energies 2020, 13, 1102. [CrossRef]
32. Li, Z.; Friedrich, D.; Harrison, G.P. Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model. Energies 2020, 13, 780. [CrossRef]
33. Culaba, A.B.; Del Rosario, A.J.R.; Ubando, A.T.; Chang, J.-S. Machine learning-based energy consumption clustering and forecasting for mixed-use buildings. Int. J. Energy Res. 2020, 44, 9659–9673. [CrossRef]
34. Wang, Z.; Wang, Y.; Zeng, R.; Srinivasan, R.S.; Ahrentzen, S. Random Forest based hourly building energy prediction. Energy Build. 2018, 171, 11–25. [CrossRef]
35. Sauer, J.; Mariani, V.C.; dos Santos Coelho, L.; Ribeiro, M.H.D.M.; Rampazzo, M. Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings. Evol. Syst. 2021, 1–12. [CrossRef]
36. Bassi, A.; Shenoy, A.; Sharma, A.; Sigurdson, H.; Glossop, C.; Chan, J.H. Building energy consumption forecasting: A comparison of gradient boosting models. In Proceedings of the 12th International Conference on Advances in Information Technology, Bangkok, Thailand, 29 June–1 July 2021. [CrossRef]
37. Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Perez, O.; Gonzalez-Morales, L.; SantosGarcía, F. A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings. Appl. Sci. 2021, 11, 7886. [CrossRef]
38. 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]
39. Lemos, V.H.B.; Almeida, J.D.S.; Paiva, A.C.; Junior, G.B.; Silva, A.C.; Neto, S.M.B.; Lima, A.C.M.; Cipriano, C.L.S.; Fernandes, E.C.; Silva, M.I.A. Temporal convolutional network applied for forecasting individual monthly electric energy consumption. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 2002–2007.
40. Bendaoud, N.M.M.; Farah, N. Using deep learning for short-term load forecasting. Neural Comput. Appl. 2020, 32, 15029–15041. [CrossRef]
41. Gao, Y.; Ruan, Y.; Fang, C.; Yin, S. Deep learning and transfer learning models of energy consumption forecasting for a building with poor information data. Energy Build. 2020, 223, 110156. [CrossRef]
42. Bifet, A.; Gavaldà, R. Learning from time-changing data with adaptive windowing. In Proceedings of the 7th SIAM International Conference on Data Mining, Minneapolis, MN, USA, 26–28 April 2007; pp. 443–448.
43. Moon, J.; Kim, Y.; Son, M.; Hwang, E. Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron. Energies 2018, 11, 3283. [CrossRef]
44. Khosravani, H.; Castilla, M.; Berenguel, M.; Ruano, A.; Ferreira, P. A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building. Energies 2016, 9, 57. [CrossRef]
45. Ali, U.; Shamsi, M.H.; Bohacek, M.; Hoare, C.; Purcell, K.; Mangina, E.; O’Donnell, J. A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings. Appl. Energy 2020, 267, 114861. [CrossRef]
46. Andelkovi´c, A.S.; Bajatovi´c, D. Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas ¯ consumption prediction. J. Clean. Prod. 2020, 266, 122096. [CrossRef]
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spelling Mariano-Hernández, DeyslenHernández Callejo, LuisSolís, MartínZorita Lamadrid, Angel LuisDuque-Perez, OscarGonzalez Morales, Luis GerardoSantos Garcia, FelixJaramillo Duque, ÁlvaroOspino C., AdalbertoAlonso Gómez, VíctorBello, Hugo J.2022-08-24T14:53:53Z2022-08-24T14:53:53Z2022-05-12Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857https://hdl.handle.net/11323/9472https://doi.org/10.3390/su1410585710.3390/su141058572071-1050Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.14 páginasapplication/pdfengMDPI AGSwitzerlandAtribución 4.0 Internacional (CC BY 4.0)© 2022 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_abf2Analysis of the integration of drift detection methods in learning algorithms for electrical consumption forecasting in smart buildingsArtículo 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/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.mdpi.com/2071-1050/14/10/5857Sustainability1. IEA Tracking Buildings. 2021. Available online: https://www.iea.org/reports/tracking-buildings-2021 (accessed on 16 March 2022).2. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Guz, Ł.; Bocian, M.; Balaras, C.A. An easy and widelapplicable forecast control for heating systems in existing and new buildings: First field experiences. J. Clean. Prod. 2022, 352, 131605. [CrossRef]3. Devagiri, V.M.; Boeva, V.; Abghari, S.; Basiri, F.; Lavesson, N. Multi-view data analysis techniques for monitoring smart building systems. Sensors 2021, 21, 6775. [CrossRef] [PubMed]4. Izidio, D.M.; de Mattos Neto, P.S.; Barbosa, L.; de Oliveira, J.F.; Marinho, M.H.D.N.; Rissi, G.F. Evolutionary hybrid system for energy consumption forecasting for smart meters. Energies 2021, 14, 1794. [CrossRef]5. Hong, T.; Wang, Z.; Luo, X.; Zhang, W. State-of-the-art on research and applications of machine learning in the building life cycle. Energy Build. 2020, 212, 109831. [CrossRef]6. Kim, J.Y.; Cho, S.B. Electric energy consumption prediction by deep learning with state explainable autoencoder. Energies 2019, 12, 739. [CrossRef]7. Zeng, A.; Ho, H.; Yu, Y. Prediction of building electricity usage using Gaussian Process Regression. J. Build. Eng. 2020, 28, 101054. [CrossRef]8. Xu, W.; Peng, H.; Zeng, X.; Zhou, F.; Tian, X.; Peng, X. A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl. Intell. 2019, 49, 3002–3015. [CrossRef]9. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Anasiewicz, R.; Balaras, C.A. A simple building energy model in form of an equivalent outdoor temperature. Energy Build. 2021, 236, 110766. [CrossRef]10. Žliobaite, I.; Pechenizkiy, M.; Gama, J. ˙ An Overview of Concept Drift Applications BT—Big Data Analysis: New Algorithms for a New Society; Japkowicz, N., Stefanowski, J., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 91–114, ISBN 978-3-319-26989-4.11. Iwashita, A.S.; Papa, J.P. An Overview on Concept Drift Learning. IEEE Access 2019, 7, 1532–1547. [CrossRef]12. Baier, L.; Kühl, N.; Satzger, G.; Hofmann, M.; Mohr, M. Handling concept drifts in regression problems—the error intersection approach. In WI2020 Zentrale Tracks; GITO Verlag: Berlin, Germany, 2020; pp. 210–224.13. Kahraman, A.; Kantardzic, M.; Kahraman, M.; Kotan, M. A data-driven multi-regime approach for predicting energy consumption. Energies 2021, 14, 6763. [CrossRef]14. Webb, G.I.; Lee, L.K.; Goethals, B.; Petitjean, F. Analyzing concept drift and shift from sample data. Data Min. Knowl. Discov. 2018, 32, 1179–1199. [CrossRef]15. Lu, J.; Liu, A.; Dong, F.; Gu, F.; Gama, J.; Zhang, G. Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 2019, 31, 2346–2363. [CrossRef]16. Brzezinski, D.; Stefanowski, J. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25, 81–94. [CrossRef]17. 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[CrossRef]1411014Drift detectionElectrical consumption forecastingEnergy forecastingMachine learningSmart buildingsPublicationORIGINALAnalysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings.pdfAnalysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings.pdfapplication/pdf2596732https://repositorio.cuc.edu.co/bitstreams/16fb8359-02be-4ab6-af7f-41ed3ac28797/download4b9a8d706c4e2403b7aa38243caff4f8MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/c02603c1-1a76-4c86-b88c-fb7c2b145797/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTAnalysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings.pdf.txtAnalysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption 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