Solar irradiance is the measurement of the amount of power from the Sun per unit area. Solar irradiance has a very high degree of variability, due to many environmental factors, including cloud cover, relative humidity, and air temperature. Predicting solar irradiance is very useful for measuring future solar energy production and power scheduling. Real-time solar irradiance can be forecasted using either machine learning or physics-based models, both having their own respective trade-offs. To overcome the limitations of both machine learning and physics-based simulations, we propose a novel framework in predictive analytics that combines the power of both types of models. In particular, we propose a concept-drift framework to develop a model that integrates satellite cloud imagery with real-time solar irradiance measurements to improve irradiance forecasting. We present preliminary results using real-world irradiance and cloud data to support the motivation for a concept-drift based approach. Based on our results, we found that one static model cannot fully capture the dynamics of solar irradiance on both days where cloud cover is present and days where there is no cloud cover.
Solar irradiance forecasting
