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dc.contributor.authorJurado Gómez, Sergio
dc.contributor.authorNebot Castells, M. Àngela
dc.contributor.authorMúgica Álvarez, Francisco
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-01-26T12:48:06Z
dc.date.available2016-01-26T12:48:06Z
dc.date.issued2015
dc.identifier.citationJurado, S., Nebot, M., Múgica, F. A flexible fuzzy inductive reasoning approach for load modelling able to cope with missing data. A: International Conference on Simulation Tools and Techniques. "SIMUTools '15: proceedings of the 8th International Conference on Simulation Tools and Techniques". Atenas: Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), 2015, p. 349-356.
dc.identifier.isbn978-1-63190-079-2
dc.identifier.urihttp://hdl.handle.net/2117/82054
dc.description.abstractLoad forecasting in buildings and homes has been in recent years a task of increasing importance. New services and functionalities can be offered in the home environment due to this predictions, for instance, the detection of potential demand response programs and peaks that may increase the energy bill in a dynamic tariff framework. Almost real-time predictions are key for these services but missing values can dramatically affect the performance of the energy forecasting or distort the prediction significantly. Fuzzy Inductive Reasoning has been proven to model load consumptions with high accuracy compared to other typical AI and statistical techniques. Nevertheless, it has several limitations when missing data is presented in the training data of the model and during prediction. In this paper, we present an improved version of Fuzzy Inductive Reasoning, called Flexible FIR Prediction that can cope with missing information in the input pattern as well as, in situations where patterns are not found in the behaviour matrix. The new technique has been tested with real data from one building of the Universitat Politècnica de Catalunya (UPC) and the results show that Flexible FIR Prediction is able to generate good predictions with low errors (less than 15%) although missing data is present in the training and online prediction phases.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
dc.subject.lcshKnowledge representation (Information theory)
dc.subject.otherPrediction with missing values
dc.subject.otherFuzzy inductive reasoning
dc.subject.otherEnergy modelling
dc.titleA flexible fuzzy inductive reasoning approach for load modelling able to cope with missing data
dc.typeConference report
dc.subject.lemacRepresentació del coneixement (Teoria de la informació)
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.4108/eai.24-8-2015.2261022
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://dl.acm.org/citation.cfm?id=2832238
dc.rights.accessOpen Access
local.identifier.drac17411661
dc.description.versionPostprint (author's final draft)
local.citation.authorJurado, S.; Nebot, M.; Múgica, F.
local.citation.contributorInternational Conference on Simulation Tools and Techniques
local.citation.pubplaceAtenas
local.citation.publicationNameSIMUTools '15: proceedings of the 8th International Conference on Simulation Tools and Techniques
local.citation.startingPage349
local.citation.endingPage356


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