Ensemble learning for forecasting main meteorological parameters

The significant role of predicting weather conditions in daily life, the new era of innovative machine learning approaches along with the availability of high volumes of data and high computer performance capabilities, creates increasing perspectives for novel improved short-range forecasting of main meteorological parameters. Among the various algorithms for forecasting parameters, ensemble learning approaches are able to generate simple models which provide accurate predictions for regression problems. The advantage of ensembles with respect to single models is that they perform remarkably well for a variety of problems. The main aim of this ongoing research is to provide some preliminary assessment of the applicability of ensemble learning for wind speed forecasting. In this work, forecasting results of a single and two ensemble models are presented and compared.