Comparative Performance Analysis of Machine Learning-Based Annual and Seasonal Approaches for Power Output Prediction in Combined Cycle Power Plants

This study develops an innovative framework that utilizes real-time operational data to forecast electrical power output (EPO) in Combined Cycle Power Plants (CCPPs) by employing a temperature segmentation-based modeling approach. Instead of using a single general prediction model, which is commonly seen in the literature, three separate prediction models were created to explicitly capture the nonlinear effect of ambient temperature (AT) on efficiency (AT < 12 °C, 12 °C ≤ AT < 20 °C, AT ≥ 20 °C). Linear Ridge, Medium Tree, Rational Quadratic Gaussian Process Regression (GPR), Support Vector Machine (SVM) Kernel, and Neural Network methods were applied. In the modeling, the variables considered were AT, relative humidity (RH), atmospheric pressure (AP), and condenser vacuum (V). The highest performance was achieved with the Rational Quadratic GPR method. In this approach, the weighted average Mean Absolute Error (MAE) was found to be 2.225 with seasonal segmentation, while it was calculated as 2.417 in the non-segmented model. By applying seasonal prediction models, the hourly EPO prediction error was reduced by 192 kW, achieving a 99.77% average convergence of the predicted power output values to the actual values. This demonstrates the contribution of the proposed approach to enhancing operational efficiency.