Synthetic Data Generation for Deep Learning based Renewable Energy Forecasts

Renewable Energy is gaining more importance in tackling globally growing energy demands. However, time-series obtained form production sites are often limited in time-horizon and also different in nature due to present technology in power plants. Due to their high resource demands high-resolution NWPs, such as AROME, can often not be fully stored long-term and change significantly in each update cycle, making them a challenging input for data hungry deep learning approaches. Still, in short- and medium range forecasts selected NWP parameters often improve forecast results of machine learning approaches.

In this study, we investigate how to setup a synthetically generated/extended training dataset based on diverse spatially and temporary heterogeneous inputs. For practical reasons, we concentrate on a solar power production case study. Data considered includes NWP models (e.g.: AROME, ECMWF), satellite data and products (e.g.:  CAMS), radiation time series from remote sensing, and observation time-series. In particular, we present a machine learning methodology on extending time-series from AROME in accordance with close observations.  Our synthetic data generator learns from long observation time series, and longer available but still coarse ECMWF. It yields both synthetic observations for missing data in the observation, synthetic production optimized for a relatively new established site, as well as a prolongation of numeric model data for studied renewable energy sites.

Results are evaluated by our deep learning framework (e.g.: LSTM) and cross-validation or achieved data of the data-set. The combination of real and synthetic data generally offers benefits in forecasting by more complex machine learning methods relying on sufficiently long training data-sets.