Deep learning approaches for multi-site wind power forecasting in the West Aegean region of Turkey

Wind energy is one of the most economical and promising ways of producing renewable energy among today's technologies. But the uncertainties arising from the chaotic nature and the variability of weather events are major problems to the market stabilization and regular maintenance of the wind power systems. Wind farms are usually built in high wind speed potential areas. West Marmara and Aegean regions have the highest number of wind farms in Turkey. Forecasting the wind power production in a region is usually done separately for each wind farm, but forecasting in multi-site context contains more spatial information, thus enables learning from the neighbor wind plants. In this thesis, incorporation of multi-site spatial information, besides the temporal information, to deep learning models is studied. Six alternative deep learning methods, i.e. multilayer perceptrons, recurrent networks, graph neural networks, convolutional networks and their variants are implemented for this purpose. Each model is enhanced with numerical weather predictions to create more accurate long term forecasts, and model parameters are tuned with a hyperparameter optimization. Finally, these models are compared with tree-based boosting, penalized regression and persistence benchmarks over a one-year period. In order to investigate the positive effect of using multi-site approach, recurrent model is trained both separately for each plant and for all the plants at the same time in a multi-site context. Single plant based recurrent model performed better than the multi-site recurrent model, but methods using convolutional layers, significantly outperforms single recurrent, benchmarks and remaining deep learning models.