KaspaFog: uma abordagem na névoa para o gerenciamento de fontes e cargas de eletricidade de uma Microgrid com foco na redução energética

• Eric B. Barros UFBA
• Wesley O. Souza UFBA
• Matheus T. M. Barbosa UFBA
• Bruno G. Batista UNIFEI
• Bruno T. Kuehne UNIFEI
• Dionisio Leite UFMS
• Maycon L. M. Peixoto UFBA

DOI: https://doi.org/10.5753/sbrc.2021.16730

Abstract

In Microgrids the energy is produced by combining renewable and non-renewable sources. Thus, it is essential to control non-renewable generation to avoid waste. This type of problem has been investigated by several researches, which use variations in the adjustment of the proportional–integral–derivative (PID) controller to avoid energy losses. However, none of the works employed a strategy to reduce the time that power generators are in balance. In this context, the paper presents KaspaFog, an approach that employs a data prediction strategy using the SARIMA model and a neural network with reinforcement learning to adjust the energy generation control. KaspaFog is an infrastructure in the fog supported by the cloud, due to the need for processing and fast response times. KaspaFog reaches 18% reduction in non-renewable compared to the Ziegler-Nichols adjustment.

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