A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters

Abstract

Wave Energy is one of the renewable sources with greatest potential. Since power coming from waves fluctuates, the grid integration of wave energy involves several power conditioning stages to comply with grid quality requirements. However, to ensure full integration of wave energy in a smart grid scenario and unlock advanced monitoring and control techniques (e.g. Demand/Response), it is crucial to forecast the output power. This work proposes a methodology to forecast in short-term horizons (i.e. 15 min to 240 min) the power delivered to the grid of the Inertial Sea Wave Energy Converter (ISWEC), a device that harnesses wave power through the inertial effect of a gyroscope. Therefore, we designed, optimized and compared the performance of five known machine learning techniques for time series point forecasting: Random Forest, Support Vector Regression, Long Short-Term Memory Neural Network, Transformer Neural Network and 1 Dimensional Convolutional Neural Network. Additionally, we studied the efficacy of downsampling technique aggregating original dataset sampled every 0.1 s in time steps of 1min, 3min, 5min and 15min to compare the performance behaviour of the different machine learning models for these datasets. Furthermore, we implemented Prediction Intervals (PIs) to calculate the inherent uncertainties associated with the previously mentioned machine learning techniques. These PIs were built based on the Non-Parametric Kernel Density Estimator technique. The point forecasting and the PIs results showed that models’ performance improved as the downsampling increased. Moreover, the Random Forest model was the worst-performing in all cases. Finally, none of the other models can be considered the best overall.