Novel machine learning methods to enhance wind power probabilistic forecasting

Abstract

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 success in different fields attracted research into wind power probabilistic forecasting. The objective of the research was to enhance wind power forecasting through data-driven or machine learning models, proposing a new framework as an alternative to the existing ones, validated in different climate regions using grid-like topology data (weather images). The project focused on the European Energy Markets 2020 Conference (EEM20) Forecasting Competition. The setting emulates the day-ahead electricity market and the location consisted of all four electricity price regions in Sweden, where three main sets of data were provided. Three main challenges are present in this competition, namely the large volume of data (the curse of dimensionality), the lack of information regarding wind turbine availability, and the growing evolution of wind installed capacity between data sets. The methodology to create a framework consisted of evaluating different machine learning approaches, feature engineering, and tuning the final model for every region. The final framework is called the Smooth Pinball Hybrid Neural Network (SPinHy-NN). The results showcased a suitable CNN architecture, providing more accurate forecasts compared to MLP and k-means clustering. The quantile cross-over problem has been evaluated through the crossing loss and the number of crossings metrics. The analysis shows that a margin parameter can correct this undesired behavior. The conclusions validate the SPinHy-NN as a generalized framework for different climates through the final score achieved post-competition, managing to capture the spatial patterns in most cases. Moreover, the framework can be adapted to reach a desirable trade-off between accuracy, sharpness, and consistency. Recommendations aim to extend this research by employing satellite imaging, further feature engineering, novel neural networks, and exploring spatiotemporal models to capture atmospheric dynamics.