Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application

Dealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and BEMS applications, where the information from home area sensors and/or smart meters is sometimes missing, w...

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Autores: Jurado, Sergio, Nebot Castells, M. Àngela|||0000-0002-4621-8262, Múgica Álvarez, Francisco|||0000-0003-2843-0427, Mihaylov, Mihail
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/101525
Acceso en línea:https://hdl.handle.net/2117/101525
https://dx.doi.org/10.1016/j.asoc.2016.11.040
Access Level:acceso abierto
Palabra clave:Soft computing
Fuzzy inductive reasoning
Entropy-based feature selection
Prediction with missing values
Energy modelling
Informàtica tova
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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oai_identifier_str oai:upcommons.upc.edu:2117/101525
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spelling Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid applicationJurado, SergioNebot Castells, M. Àngela|||0000-0002-4621-8262Múgica Álvarez, Francisco|||0000-0003-2843-0427Mihaylov, MihailSoft computingFuzzy inductive reasoningEntropy-based feature selectionPrediction with missing valuesEnergy modellingInformàtica tovaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialDealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and BEMS applications, where the information from home area sensors and/or smart meters is sometimes missing, which may hinder or even prevent the forecasting of the next hours and days. In concrete, we focus in a Soft Computing technique called Fuzzy Inductive Reasoning (FIR) and its improved version that can cope with missing information in the forecasting process: flexible FIR. In this article eight different strategies for flexible FIR forecasting are defined and studied taking into account: causal relevance of input variables, consistency of predictions, inertia criterion and K-Nearest Neighbours. Furthermore, we evaluate the implications of prediction accuracy and number of predictions, when the number of Missing Values (MVs) in the training dataset is increased progressively. To this end, a real smart grid forecasting application, i.e. electricity load forecasting, has been chosen in this study. The results show that all eight strategies proposed are able to cope with MVs and take advantage of the inherent information in the data, with better results in those strategies making use of causal relevance. In addition, the robustness of flexible FIR and its eight strategies are proved taking into account that the percentage of electricity load predictions from the test dataset is on average 96.15% when the %MVs in training dataset was around 73%.Peer Reviewed20172017-02-0120172017-02-24journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/101525https://dx.doi.org/10.1016/j.asoc.2016.11.040reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1015252026-05-27T15:37:01Z
dc.title.none.fl_str_mv Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
title Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
spellingShingle Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
Jurado, Sergio
Soft computing
Fuzzy inductive reasoning
Entropy-based feature selection
Prediction with missing values
Energy modelling
Informàtica tova
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
title_full Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
title_fullStr Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
title_full_unstemmed Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
title_sort Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application
dc.creator.none.fl_str_mv Jurado, Sergio
Nebot Castells, M. Àngela|||0000-0002-4621-8262
Múgica Álvarez, Francisco|||0000-0003-2843-0427
Mihaylov, Mihail
author Jurado, Sergio
author_facet Jurado, Sergio
Nebot Castells, M. Àngela|||0000-0002-4621-8262
Múgica Álvarez, Francisco|||0000-0003-2843-0427
Mihaylov, Mihail
author_role author
author2 Nebot Castells, M. Àngela|||0000-0002-4621-8262
Múgica Álvarez, Francisco|||0000-0003-2843-0427
Mihaylov, Mihail
author2_role author
author
author
dc.subject.none.fl_str_mv Soft computing
Fuzzy inductive reasoning
Entropy-based feature selection
Prediction with missing values
Energy modelling
Informàtica tova
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Soft computing
Fuzzy inductive reasoning
Entropy-based feature selection
Prediction with missing values
Energy modelling
Informàtica tova
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description Dealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and BEMS applications, where the information from home area sensors and/or smart meters is sometimes missing, which may hinder or even prevent the forecasting of the next hours and days. In concrete, we focus in a Soft Computing technique called Fuzzy Inductive Reasoning (FIR) and its improved version that can cope with missing information in the forecasting process: flexible FIR. In this article eight different strategies for flexible FIR forecasting are defined and studied taking into account: causal relevance of input variables, consistency of predictions, inertia criterion and K-Nearest Neighbours. Furthermore, we evaluate the implications of prediction accuracy and number of predictions, when the number of Missing Values (MVs) in the training dataset is increased progressively. To this end, a real smart grid forecasting application, i.e. electricity load forecasting, has been chosen in this study. The results show that all eight strategies proposed are able to cope with MVs and take advantage of the inherent information in the data, with better results in those strategies making use of causal relevance. In addition, the robustness of flexible FIR and its eight strategies are proved taking into account that the percentage of electricity load predictions from the test dataset is on average 96.15% when the %MVs in training dataset was around 73%.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-02-01
2017
2017-02-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/101525
https://dx.doi.org/10.1016/j.asoc.2016.11.040
url https://hdl.handle.net/2117/101525
https://dx.doi.org/10.1016/j.asoc.2016.11.040
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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