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dc.contributor.authorJurado Gómez, Sergio
dc.contributor.authorNebot Castells, M. Àngela
dc.contributor.authorMúgica Álvarez, Francisco
dc.contributor.authorMihaylov, Mihail
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.identifier.citationJurado, S., Nebot, M., Múgica, F., Mihaylov, M. Fuzzy inductive reasoning forecasting strategies able to cope with missing data: A smart grid application. "Applied soft computing", Febrer 2017, vol. 51, p. 225-238.
dc.description.abstractDealing 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%.
dc.format.extent14 p.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshBuildings -- Energy conservation
dc.subject.lcshFuzzy systems
dc.subject.lcshMachine learning
dc.subject.lcshSoft computing
dc.subject.otherFuzzy inductive reasoning
dc.subject.otherEntropy-based feature selection
dc.subject.otherPrediction with missing values
dc.subject.otherEnergy modelling
dc.titleFuzzy inductive reasoning forecasting strategies able to cope with missing data: A smart grid application
dc.subject.lemacEdificis -- Estalvi d'energia
dc.subject.lemacSistemes borrosos
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacInformàtica tova
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorJurado, S.; Nebot, M.; Múgica, F.; Mihaylov, M.
local.citation.publicationNameApplied soft computing

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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain