Investigating the Use of Reservoir Computing for Forecasting the Hourly Wind Speed in Short-Term

This paper presents the results of the models created for forecasting the hourly wind speed in 24-step-forward using Reservoir Computing (RC). RC is a new paradigm that offers an intuitive methodology for using the temporal processing power of recurrent neural networks (RNN) without the inconvenience of training them. Originally, introduced independently as liquid State Machine 151 or Echo State Network 161, whose basic concept is randomly construct a RNN and leave the weights unchanged. In this work we used Echo State Network (ESN) to create the models and Multi-Layer Networks (MLP) to compare the results. The results showed that the ESN performed significantly better than MLP networks, even though it presents a significantly simpler, and faster, training algorithm.