Accurate probabilistic forecasts of renewable generation are the driving force for optimizing the operation and management of MicroGrid systems. Combining forecasts of different individual models can improve forecast accuracy, but unlike combining point forecasts, for which simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more complex task. Today, ensembles of forecasting models are one of the promising directions for problem solving, where forecasting accuracy is more important than the ability to interpret the model. The main idea of ensembles is the training of several basic models and the aggregation of the results of their work. Empirical studies show that combinations of forecasts, on average, are more likely to produce better forecasts than methods that are based on selecting only one forecasting model. When building ensembles, the issue of ensuring diversity of models and effective training of model members of the ensemble becomes especially relevant. The article is devoted to solving the issues of building an ensemble model for forecasting photovoltaic (PV) power, which combines the results of several basic probabilistic models. Using the ensemble method proposed by the authors can improve forecasting accuracy and reduce the time required for training and evaluation of ensemble member models. Directions and prospects of further research are formulated.