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A novel method for real-time solar generation forecast using weather data,while exploiting both spatial and temporal structural dependencies is proposed.The network observed over time is projected to a lower-dimensionalrepresentation where a variety of weather measurements are used to train astructured regression model while weather forecast is used at the inferencestage. Experiments were conducted at 288 locations in the San Antonio, TX areaon obtained from the National Solar Radiation Database. The model predictssolar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for thewinter, and 0.89 for the global model). The best accuracy was obtained by theRandom Forest Regressor. Multiple experiments were conducted to characterizeinfluence of missing data and different time horizons providing evidence thatthe new algorithm is robust for data missing not only completely at random butalso when the mechanism is spatial, and temporal.

Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast