Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

• https://www.sciencedirect.com/science/article/pii/S0306261923003537

solar power forecasting, photovoltaic dataset, prediction uncertainty, machine
learning model

A growing interest in renewable power increases its impact on the energy grid,
posing significant challenges to reliability, stability, and planning. Although
the use of weather-based prediction methods helps relieve these issues, their
real-world accuracy is limited by the errors inherent to the weather forecast
data used during the inference. To help resolve this limitation, we introduce the
SolarPredictor model. It uses a hybrid convolutional architecture combining
residual connections with multi-scale spatiotemporal analysis, predicting solar
power from publicly available high-uncertainty weather forecasts. Further, to
train the model, we present the SolarDB dataset comprising one year of power
production data for 16 solar power plants. Crucially, we include weather
forecasts with seven days of hourly history, allowing our model to anticipate
errors in the meteorological features. In contrast to previous work, we evaluate
the prediction accuracy using widely available low-precision weather forecasts,
accurately reflecting the real-world performance. Comparing against 17 other
techniques, we show the superior performance of our approach, reaching an average
RRMSE of 6.15 for 1-day, 8.54 for 3-day, and 8.89 for 7-day predictions on the
SolarDB dataset. Finally, we analyze the effects of weather forecast uncertainty
on the prediction accuracy, showing a 23 % performance gap compared to using
zero-error weather. Data and additional resources are available at
cphoto.fit.vutbr.cz/solar.