Intelligent energy management for micro-grid based on deep learning LSTM prediction model and fuzzy decision-making

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In this paper, we present a new intelligent system based on multi-agent system for energy management in micro-grid with grid-connected mode and mainly based on wind energy. The main contribution of this work is to present a model for wind power prediction for the next hour using the wind speed values of previous hours. It is a model based on a deep recurrent neural network with Long Short Term Memory which takes as input a number of previous values of the wind speed and as output the wind power produced for the next hour. The proposed model is compared to a Multi-Layer Perceptron with the same inputs and output. Both models aim to find a hidden correlation between historical weather data (previous wind speeds) and the next wind power produced. Using as a case study the dataset from a real site located in Tetouan city in Morocco, experimental results show that Long Short Term Memory based prediction model yields good results and successfully achieve high accuracy compared to Multi-Layer Perceptron based model. To establish a robust energy management strategy, a fuzzy logic controller is used as decision-making tool to manage the different energy flows in the micro-grid and to control the amount of electricity delivered or taken from the distribution network in order to reduce the costs and maximize the profits.