A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment.
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Data
2016
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Resumo
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.
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Load forecasting, Smart grids, Microgrids, Fuzzy logics
Citação
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.