Multi-step ahead ultra-short-term wind power forecasting: A forecast quality and value comparison between proposed deep learning models and an operational numerical weather prediction based model

The ongoing large scale adoption of wind power increases the associated risks related to the variability. An essential way to mitigate these risks for a utility company is to forecast production accurately. This study aims to create insight into the potential of deep learning models for both forecast quality and value on the ultra-short-term...

Novel machine learning methods to enhance wind power probabilistic forecasting: SPinHy-NN framework proposal for European electricity markets

The increasing penetration of weather-dependent energy sources brings additional challenges to the operation of the power system. Wind power forecasting is a valuable resource for these power operators: a tool that aids the decision-making process and facilitates risk management. On the other hand, the progress of machine learning and their...