On the classification of weather based on the production of photovoltaic installations

In recent years, there has been an energy transition in which fossil fuel-fired power plants are being replaced by renewable energy sources such as photovoltaics. While the influence of factors such as location and method of installation or the sun’s position above the horizon is deterministic, the weather factor is rather random. Weather data can be easily obtained from nearby weather stations, but some weather phenomena are so dynamic that data obtained in this way can become useless. When considering the problem of classifying weather conditions based on production data of photovoltaic installations, we propose two new features extracted from the production data that describe the overall level of sunshine and the variability of sunlight. Three supervised learning methods were used to classify the dataset: CNN, Random Forest classifier and Decision Tree Classifier. Reference values were obtained by generating classification results from the raw output and compared with those generated from the two new features. The experiments showed a statistically significant advantage for the models fitted to the data extended with the new features.