{'fraction_non_alphanumeric': 0.01996007984031936, 'fraction_numerical': 0.001996007984031936, 'mean_word_length': 5.229813664596273, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Support Vector Regression for Solar Power Prediction

In recent years, renewable energies have been covering an increasing part of the worldwide electrical power demand. The additional volatility introduced to power grids by weather dependent renewable energy sources, i.e., wind and solar, makes it necessary to improve the accuracy of energy forecasts, so that the electrical grid can be operated in a cost-efficient way. The focus of this thesis lies on improving forecast models for short-term PV power predictions with horizons ranging from 15 minutes to five hours. For this, recent power measurements of PV systems throughout Germany are combined with data from weather forecast approaches. Apart from commonly used numerical weather prediction model output, satellite-based cloud motion vector predictions are applied. The data sets of these sources are preprocessed and combined with machine learning. In particular, support vector regression is optimized to work as the main regression model.