Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9366
Title: A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment.
Authors: Coelho, Vitor Nazário
Coelho, Igor Machado
Coelho, Bruno Nazário
Reis, Agnaldo José da Rocha
Souza, Marcone Jamilson Freitas
Guimarães, Frederico Gadelha
Keywords: Load forecasting
Smart grids
Microgrids
Fuzzy logics
Issue Date: 2016
Citation: COELHO, V. N. et al. A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment. Applied Energy, v. 169, p. 567-584, 2016. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0306261916301684>. Acesso em: 16 jan. 2018.
Abstract: The importance of load forecasting has been increasing lately and improving the use of energy resources remains a great challenge. The amount of data collected from Microgrid (MG) systems is growing while systems are becoming more sensitive, depending on small changes in the daily routine. The need for flexible and adaptive models has been increased for dealing with these problems. In this paper, a novel hybrid evolutionary fuzzy model with parameter optimization is proposed. Since finding optimal values for the fuzzy rules and weights is a highly combinatorial task, the parameter optimization of the model is tackled by a bio-inspired optimizer, so-called GES, which stems from a combination between two heuristic approaches, namely the Evolution Strategies and the GRASP procedure. Real data from electric utilities extracted from the literature are used to validate the proposed methodology. Computational results show that the proposed framework is suitable for short-term forecasting over microgrids and large-grids, being able to accurately predict data in short computational time. Compared to other hybrid model from the literature, our hybrid metaheuristic model obtained better forecasts for load forecasting in aMG scenario, reporting solutions with low variability of its forecasting errors.
URI: http://www.repositorio.ufop.br/handle/123456789/9366
metadata.dc.identifier.uri2: https://www.sciencedirect.com/science/article/pii/S0306261916301684
ISSN: 03062619
Appears in Collections:DECOM - Artigos publicados em periódicos

Files in This Item:
File Description SizeFormat 
ARTIGO_SelfAdaptiveEvolutionary.pdf1,51 MBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.