Coelho, Vitor NazárioCoelho, Igor MachadoCoelho, Bruno NazárioReis, Agnaldo José da RochaEnayatifar, RasulSouza, Marcone Jamilson FreitasGuimarães, Frederico Gadelha2016-08-092016-08-092016COELHO, 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: <http://www.sciencedirect.com/science/article/pii/S0306261916301684>. Acesso em: 11 jul. 2016.0306-2619http://www.repositorio.ufop.br/handle/123456789/6785The 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.en-USabertoLoad forecastingSmart gridsMicrogridsFuzzy logicsHybrid forecasting modelArtigo publicado em periodicoO periódico Applied Energy concede permissão para depósito deste artigo no Repositório Institucional da UFOP. Número da licença: 3914200862502.https://doi.org/10.1016/j.apenergy.2016.02.045