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Fuzzy inductive reasoning forecasting strategies able to cope with missing data: A smart grid application
dc.contributor.author | Jurado Gómez, Sergio |
dc.contributor.author | Nebot Castells, M. Àngela |
dc.contributor.author | Múgica Álvarez, Francisco |
dc.contributor.author | Mihaylov, Mihail |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2018-03-01T11:00:59Z |
dc.date.available | 2018-12-05T01:30:59Z |
dc.date.issued | 2017-02 |
dc.identifier.citation | Jurado, 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.identifier.issn | 1568-4946 |
dc.identifier.uri | http://hdl.handle.net/2117/114674 |
dc.description.abstract | 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%. |
dc.format.extent | 14 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Buildings -- Energy conservation |
dc.subject.lcsh | Fuzzy systems |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Soft computing |
dc.subject.other | Fuzzy inductive reasoning |
dc.subject.other | Entropy-based feature selection |
dc.subject.other | Prediction with missing values |
dc.subject.other | Energy modelling |
dc.title | Fuzzy inductive reasoning forecasting strategies able to cope with missing data: A smart grid application |
dc.type | Article |
dc.subject.lemac | Edificis -- Estalvi d'energia |
dc.subject.lemac | Sistemes borrosos |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Informàtica tova |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1016/j.asoc.2016.11.040 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1568494616306093 |
dc.rights.access | Open Access |
local.identifier.drac | 21985413 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Jurado, S.; Nebot, M.; Múgica, F.; Mihaylov, M. |
local.citation.publicationName | Applied soft computing |
local.citation.volume | 51 |
local.citation.startingPage | 225 |
local.citation.endingPage | 238 |
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