Forecasting system with sub-model selection strategy for photovoltaic power output forecasting

Photovoltaic power output forecasting has been focused on worldwide due to its environmental benefits and soaring load demand of the electricity market. Many forecasting technologies have been developed to increase photovoltaic power output forecasting performance. However, due to the various characteristics of different photovoltaic power output time series, no commonly used technology can always reach satisfactory prediction performance. To solve this dilemma and further improve photovoltaic power output forecasting accuracy and stability, a novel photovoltaic power output forecasting system is developed, where the data preprocessing method is first used to capture the primary characteristic of photovoltaic power output time series. Then, six forecasting models are employed to predict the preprocessed data. Sub-model selection strategy is introduced to select the best three forecasting models for obtaining good prediction results under different circumstances. Finally, the forecasting results of three forecasting models are combined based on a multi-objective grey wolf optimizer. The developed system is proved to be effective in terms of prediction accuracy and stability in three simulation experiments. Thus, the proposed system can be widely used to improve photovoltaic power output prediction performance in practical applications and it will provide valuable technical support for the operation and management of power systems.