The pdf document can be found in the releases.
A hybrid system is composed of multiple energy sources. Those with weather dependency represent uncertainty in demand provision, which could compromise the functioning of the supplied services. Therefore, the system operator needs to be able to predict the energy balance and decide the best control strategy.
Predicting multiple time series is a difficult task to be done analytically, hence artificial intelligence has been used as alternative. Historical data has to be processed but the models have great generalization capacity, adapting to the scenario.
The present work aimed to study the feasibility of predictive analysis in hybrid systems using machine learning. Wind, solar and diesel sources were addressed along with energy storage. The data used was taken from the SONDA network, provided by INPE, at the Petrolina meteorological station.
Neural Network was used as supervised learning. The algorithm applied was the Recurrent Neural Network. Python language was used with the help of libraries like numpy, sklearn and tensorflow.
Neural networks performed well in short-term horizons but degraded with longer timesteps. The predictive strategy had accuracy of 73% and was able to obtain similar results to traditional ones. There is still space for improvement in future work.
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