Fuzzy demand forecasting in a predictive control strategy for a renewable-energy based microgrid

In model based control approaches for the dynamic operation of renewable-energy based microgrid, an accurate demand forecast is crucial. However, the high level of uncertainties in the system and non-linearities make the task of prediction not easy. In this context, we propose the use of a stable Takagi & Sugeno (T&S) fuzzy model to perform the demand forecasting in a real-life microgrid located in Huatacondo, Chile. Based on real-data from the microgrid, located in northern Chile, the T&S fuzzy model was identified and compared with an adaptive neural network, showing the T&S fuzzy model better open-loop prediction capabilities. To increase the prediction capability, an analysis of the amount of historical data needed, and the frequency required for training purposes was also done. For the case study, it is suggested to use a large amount of data rather than increasing the training frequency.