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Fuzzy Optimization and Decision Making

Published in:

10-09-2019

Using local learning with fuzzy transform: application to short term forecasting problems

Authors: Vincenzo Loia, Stefania Tomasiello, Alfredo Vaccaro, Jinwu Gao

Published in: Fuzzy Optimization and Decision Making | Issue 1/2020

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Abstract

In this paper, we formally discuss a computational scheme, which combines a local weighted regression model with fuzzy transform (or F-transform for short). The latter acts as a reduction technique on the cardinality of the learning problem, resulting in a more efficient algorithm. We tested the proposed approach first through two typical benchmark problems, that is the Hénon and the Mackey–Glass chaotic time series, then we applied it to short-term forecasting problems. Short-term forecasting is important in the energy field for the management of power systems and for energy trading. Hence, we considered two typical application examples in this field, that is wind power forecasting and load forecasting. Numerical results show the effectiveness of the proposed approach through a comparison against alternative techniques.

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Title: Using local learning with fuzzy transform: application to short term forecasting problems
Authors: Vincenzo Loia
Stefania Tomasiello
Alfredo Vaccaro
Jinwu Gao
Publication date: 10-09-2019
Publisher: Springer US
Published in: Fuzzy Optimization and Decision Making / Issue 1/2020
Print ISSN: 1568-4539
Electronic ISSN: 1573-2908
DOI: https://doi.org/10.1007/s10700-019-09311-x

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