[en] Highlights: • The accuracy of power generation prediction in CCPP is improved by an ensemble model. • A stacking prediction model based on a multi-model ensemble is proposed. • The power prediction model based on stacking under environmental variables is realized. • The hyperparameters of the sub-model are optimized by the grid-search algorithm. • The proposed method provides more accurate predictions than other methods. Electric power makes a significant contribution to society. Predicting power generation is becoming increasingly important for electric power planning and energy utilization. A reliable forecasting model is necessary for accurate planning of electricity generation. The main goal of this study is to develop effective and realistic solutions for the full-load power generation prediction of combined cycle power plants. According to 9568 items of data pertaining to a combined cycle power plant in six years of its full-load operation, a prediction method based on stacking ensemble hyperparameter optimization is established. The results demonstrate that this method provides high prediction accuracy for the power plant under multiple complex environmental variables. Besides, the predictions generated using this method are compared with those of traditional machine learning methods, random forest, and other ensemble methods, as well as those cited in the literature using the same dataset. The predictions show that the proposed method offers more accurate predictions of the power generation from a combined cycle plant, which opens up a new idea for power planning and energy utilization.