Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Marín, Luis G. (Universidad de Chile)
Cruz, Nicolás (Universidad de Chile)
Sáez, Doris (Universidad de Chile)
Sumner, Mark (University of Nottingham)
Nunez, Alfredo (TU Delft Railway Engineering)

2019

Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval in terms of the information about the uncertainty modelled for a higher prediction horizon. In this paper, a new prediction interval modelling methodology based on fuzzy numbers is proposed to solve the abovementioned drawbacks. Fuzzy and neural network prediction interval models are developed based on this proposed methodology by minimizing a novel criterion that includes the coverage probability and normalized average width. The fuzzy number concept is considered because the affine combination of fuzzy numbers generates, by definition, prediction intervals that can handle uncertainty without requiring assumptions about the data distribution. The developed models are compared with a covariance-based prediction interval method, and high-quality intervals are obtained, as determined by the narrower interval width of the proposed method. Additionally, the proposed prediction intervals are tested by forecasting up to two days ahead of the load of the Huatacondo microgrid in the north of Chile and the consumption of the residential dwellings in the town of Loughborough, UK. The results show that the proposed models are suitable alternatives to electrical consumption forecasting because they obtain the minimum interval widths that characterize the uncertainty of this type of stochastic process. Furthermore, the information provided by the obtained prediction interval could be used to develop robust energy management systems that, for example, consider the worst-case scenario.

Fuzzy number
Microgrid
Neuronal network
Prediction interval
Renewable energy

http://resolver.tudelft.nl/uuid:4aaea97d-fd74-4ae0-b150-974d2592a21f

https://doi.org/10.1016/j.eswa.2018.10.043

2019-04-24

0957-4174

Expert Systems with Applications, 119, 128-141

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2019 Luis G. Marín, Nicolás Cruz, Doris Sáez, Mark Sumner, Alfredo Nunez