Master thesis : Wind Power Forecasting

Abstract

Renewable energies are challenging to forecast due to their intermittence. However, it is crucial for the energy transition to predict accurately what is going to be produced at different temporal resolution (short, mid or long term) to integrate them in the network. In this work, we investigate the short term horizon. We work in the practical setting of the day-ahead forecast for wind farms. The aim of this work is twofold: to help the transmission system operator (TSO) in its task of balancing the network and the market participants of the day-ahead spot market. Both tasks require to know what is going to be produced for the next day. In this work, we will try new Artificial Intelligence (i.e. AI) models for wind energy forecasting. We explore state-of-the-art Machine Learning and Deep Learning models like Random Forest, Extra Trees, Recurrent Neural Network (i.e. RNN) and Transformers. We also investigate new RNN cells (e.g. BRC, nBRC and hybrid). We create original architectures of RNNs and Transformers. To compare the models and assess the results, we use two datasets: the ORES and the Gefcom2014 dataset. The first dataset is built from ORES recording productions of wind farms located in Belgium and weather data produced by the MAR (Modèle Atmosphérique Régional) developed at the University of Liège. The second dataset is often used in the scientific community. Then, we perform a deep analysis of the results given by the best models on both datasets. Additionally, we provide perspectives of improvement and we discuss other interesting techniques to investigate further.