LSTM for Grid Power Forecasting in Short-Term from Wave Energy Converters.

In recent times, the consistent growth of wave energy makes it one of the most promising forms of renewable energy. Due to the intermittency and non-stationary nature of waves, the grid integration of these renewable energy sources involves a series of complex power conditioning stages to deliver grid electric power that meets the corresponding quality standards. Furthermore, to enable optimal management and operation of a smart grid power system, forecasting the wave power delivered to the grid is essential. In this paper, we present a novel approach based on Long Short-Term Memory Neural Network to forecast the wave power delivered to the grid of a Wave Energy Converter (WEC) - the ISWEC, which is a device able to harvest sea energy by exploiting the inertial effect of a gyroscope - in short-time horizons (e.g. 1min). The data for the analysis was obtained from a simulator that combines a model of the ISWEC device and the power conditioning grid integration for this particular WEC. In addition, to investigate the effectiveness of downsampling, we compared the performance behavior of the raw dataset and downsampled versions of it. The results showed that as the downsampling increases, so does the forecasting accuracy: the forecasting performance of the raw dataset returned the worst results, while the one of the dataset with the biggest downsampling studied returned the best.