Akademik Veri Yönetim Sistemi
Mathematical Modelling of Engineering Problems, cilt.12, sa.1, ss.1-8, 2025 (Scopus)
Accurate predictions of power output in Combined Cycle Power Plants (CCPPs) are crucial for improving operational efficiency and enhancing performance monitoring. This paper compares two prominent machine learning models, artificial neural networks and extreme learning machines, for the prediction of hourly electrical power output. The analysis is based on a publicly available CCPP dataset containing 9, 568 instances with key parameters like ambient temperature, atmospheric pressure, relative humidity, and exhaust vacuum. The performances of the models were compared based on standard regression metrics. The result showed that the extreme learning machine (ELM) outperformed artificial neural network (ANN) with mean squared error (MSE) of 0.26, mean absolute error (MAE) of 0.41, root mean squared error (RMSE) of 0.51, and R2 of 0.98 when both models yielded a good prediction result, against the ANN model with an MSE of 19.33, MAE of 3.52, RMSE of 4.40, and R2of 0.85. Overfitting when dealing with small datasets and necessity of preprocessing for fine-tuning performance of ANN were the potential drawbacks highlighted by the paper. Results indicate that the use of ELM is quite viable and capable for estimation with excellent accuracy and, as a consequence, may have pragmatic implications for performance optimization studies concerning CCPP and also find broad applicability in energy management studies.