High Accuracy Forecasting with Limited Input Data: Using FFNNs to Predict Offshore Wind Power Generation

Abstract

This study proposes a Feed Forward Neural Net (FFNN) to forecast renewable energy generation of marine wind parks located in Denmark. The neural network uses historical weather and power generation data for training and applies the learned pattern to forecast wind energy production. Furthermore, the study shows how to improve prediction quality by leveraging specific parameters. Especially, we study the impact of the distance and direction of the weather station related to the production site in detail. In addition, we examined various parameters of the network to improve the accuracy. The proposed model distinguishes itself from other models by the fact that the optimal validation accuracy of more than 90 percent can be reached with training data sets of only a limited size, here two months of data with hourly resolution.