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

Autor
Jurado, S.; Nebot, M.; Mugica, F.; Mihaylov, M.
Tipus d'activitat
Article en revista
Revista
Applied soft computing
Data de publicació
2017-02
Volum
51
Pàgina inicial
225
Pàgina final
238
DOI
https://doi.org/10.1016/j.asoc.2016.11.040 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/114674 Obrir en finestra nova
URL
https://www.sciencedirect.com/science/article/pii/S1568494616306093 Obrir en finestra nova
Resum
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...
Citació
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.
Paraules clau
Energy modelling, Entropy-based feature selection, Fuzzy inductive reasoning, Prediction with missing values
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants