  Accurate PhotoVoltaic (PV) power generation forecasting is vital for the
efficient operation of Smart Grids. The automated design of such accurate
forecasting models for individual PV plants includes two challenges: First,
information about the PV mounting configuration (i.e. inclination and azimuth
angles) is often missing. Second, for new PV plants, the amount of historical
data available to train a forecasting model is limited (cold-start problem). We
address these two challenges by proposing a new method for day-ahead PV power
generation forecasts called AutoPV. AutoPV is a weighted ensemble of
forecasting models that represent different PV mounting configurations. This
representation is achieved by pre-training each forecasting model on a separate
PV plant and by scaling the model's output with the peak power rating of the
corresponding PV plant. To tackle the cold-start problem, we initially weight
each forecasting model in the ensemble equally. To tackle the problem of
missing information about the PV mounting configuration, we use new data that
become available during operation to adapt the ensemble weights to minimize the
forecasting error. AutoPV is advantageous as the unknown PV mounting
configuration is implicitly reflected in the ensemble weights, and only the PV
plant's peak power rating is required to re-scale the ensemble's output. AutoPV
also allows to represent PV plants with panels distributed on different roofs
with varying alignments, as these mounting configurations can be reflected
proportionally in the weighting. Additionally, the required computing memory is
decoupled when scaling AutoPV to hundreds of PV plants, which is beneficial in
Smart Grids with limited computing capabilities. For a real-world data set with
11 PV plants, the accuracy of AutoPV is comparable to a model trained on two
years of data and outperforms an incrementally trained model.
