Study on a hybrid solar power prediction model based on a numerical weather prediction and random forests

Photovoltaic grid-connected applications pose challenges to the stable and safe operation of power systems. Solar power predicted by a hybrid model based on numerical weather prediction (NWP) and random forests (RF) in order to enable grid-connected planning to balance fluctuations. Using downward solar short-wave radiation flux, temperature, wind speed, and other meteorological parameters obtained from the numerical weather forecast model, PV power forecast models with different initial time and durations under various weather conditions by RF are established. The correlations between observed and predicted power in daily, monthly and seasonal change are analyzed and both two powers are compared under three different detailed weather types. The results show that compared with NWP-principle method, this hybrid NWP-RF method can simulate the change of solar power under different weather conditions in the middle-lower plain of the Yangtze River more accurately. This model has an acceptable amount of data computation and high prediction accuracy, so that it can apply to engineering.