Evaluation of Machine Learning Techniques for Green Energy Prediction

We evaluate Machine Learning techniques for Green energy (wind, solar and biomass) prediction based on weather forecasts. Weather is constituted by multiple attributes: temperature, cloud cover, wind speed / direction which are discrete random variables. One of our objectives is to predict the weather based on the previous weather data. Additionally we are interested in finding correlation (dependencies in order to reduce the dimensionality of the data set) between these variables, predicting missing data predict deviations in weather forecasts (for job scheduling within the green control center), finding clusters within the data (constituted by closely related variables e.g. PCA that can be used to remove redundant variables), classification, finding (non-linear using SVMs) regression models, training artificial neural networks based on the historical data so that they can be used for prediction in the future.