Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Electric power plays a significant role in society, and predicting power generation is crucial for electric power planning and energy utilization. A reliable forecasting model is necessary for accurate planning of electricity generation. This study aims to develop effective solutions for full-load power generation prediction of combined cycle power plants and proposes a method that provides more accurate predictions compared to traditional machine learning methods and other ensemble methods.