Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity

Autores
Anderson, Ibar Federico
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Ensuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Traditional centralized models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes adaptive machine-learning techniques for enhancing data integrity in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid, and only the model parameters are communicated to a central server to create an aggregated global model, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of our approach in handling complex spatiotemporal changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, our methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids of the future.
Facultad de Artes
Materia
Informática
Smart grid
Machine Learning
Load Forecasting
Smart City
Energy
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/176829

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spelling Adaptive Machine Learning Techniques for Enhancing Smart Grid Data IntegrityAnderson, Ibar FedericoInformáticaSmart gridMachine LearningLoad ForecastingSmart CityEnergyEnsuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Traditional centralized models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes adaptive machine-learning techniques for enhancing data integrity in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid, and only the model parameters are communicated to a central server to create an aggregated global model, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of our approach in handling complex spatiotemporal changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, our methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids of the future.Facultad de Artes2024-06-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/176829enginfo:eu-repo/semantics/altIdentifier/url/https://journals.gaftim.com/index.php/ijcim/article/view/386info:eu-repo/semantics/altIdentifier/issn/2790-2412info:eu-repo/semantics/altIdentifier/doi/10.54489/ijcim.v4i1.386info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:19:35Zoai:sedici.unlp.edu.ar:10915/176829Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:19:36.233SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
title Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
spellingShingle Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
Anderson, Ibar Federico
Informática
Smart grid
Machine Learning
Load Forecasting
Smart City
Energy
title_short Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
title_full Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
title_fullStr Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
title_full_unstemmed Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
title_sort Adaptive Machine Learning Techniques for Enhancing Smart Grid Data Integrity
dc.creator.none.fl_str_mv Anderson, Ibar Federico
author Anderson, Ibar Federico
author_facet Anderson, Ibar Federico
author_role author
dc.subject.none.fl_str_mv Informática
Smart grid
Machine Learning
Load Forecasting
Smart City
Energy
topic Informática
Smart grid
Machine Learning
Load Forecasting
Smart City
Energy
dc.description.none.fl_txt_mv Ensuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Traditional centralized models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes adaptive machine-learning techniques for enhancing data integrity in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid, and only the model parameters are communicated to a central server to create an aggregated global model, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of our approach in handling complex spatiotemporal changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, our methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids of the future.
Facultad de Artes
description Ensuring data integrity in smart power grids is crucial for their optimized operation and planning. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in data patterns. Traditional centralized models struggle with data privacy concerns, communication overheads, and lack of model adaptiveness. This paper proposes adaptive machine-learning techniques for enhancing data integrity in smart grids. Local machine learning models are trained on distributed private datasets across different stations of the grid, and only the model parameters are communicated to a central server to create an aggregated global model, without exchanging any raw private data. The proposed approach harnesses edge resources efficiently through decentralized on-device training while providing enhanced accuracy and personalization over centralized models. Several experiments conducted on electricity consumption data validate the effectiveness of our approach in handling complex spatiotemporal changes and generating station-specific adaptive forecasts. By adopting a decentralized approach, our methodology seeks to enhance grid resilience by preserving data privacy, mitigating security risks, and optimizing the efficiency of smart microgrid operations. The proposed solution can enable optimized capacity planning and retail pricing for sustainable grids of the future.
publishDate 2024
dc.date.none.fl_str_mv 2024-06-22
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/176829
url http://sedici.unlp.edu.ar/handle/10915/176829
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://journals.gaftim.com/index.php/ijcim/article/view/386
info:eu-repo/semantics/altIdentifier/issn/2790-2412
info:eu-repo/semantics/altIdentifier/doi/10.54489/ijcim.v4i1.386
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
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instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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