Surrogates for Fair-Weather Photovoltaic Module Output

In the field of time series analysis, the scarcity of comprehensive datasets poses a significant challenge for the development of reliable predictive models. This study addresses the difficulties in forecasting solar module outputs and enhancing data accessibility for modeling, especially in residential sectors. We propose a general method to establish a distribution of photovoltaic module parameters across a country and, from this, generate a synthetic dataset for simulation and modeling pv module output. This approach integrates multiple freely available data sources. The study is focused on Germany, utilizing the Marktstammdatenregister as its main source for the module parameter distribution. The data is then enriched using publically available data. Based upon this, a crawler is developed to gather fair-weather module outputs from the Photovoltaic Geographical Information System for training, testing, and benchmarking purposes. One benchmark has fixed locations and the second one has fixed module parameters. Additionally, we provide a data loader with artificial degradation for all datasets. In the last step we test multiple state of the art models on the dataset and show that the proposed forecasting task is not trivial. All the code and data is publically available.