Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms

• Main Authors: Afzal, Asif, Alshahrani, Saad, Alrobaian, Abdulrahman, Buradi, Abdulrajak, Khan, Sher Afghan
• Format: Article
• Language: English; English
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2021
• Subjects: TJ Mechanical engineering and machinery; TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://irep.iium.edu.my/93472/7/93472_Power%20plant%20energy%20predictions%20based%20on%20thermal%20factors.pdf; http://irep.iium.edu.my/93472/13/93472_Power%20plant%20energy%20predictions%20based%20on%20thermal%20factors%20using%20ridge%20and%20support%20vector%20regressor%20algorithms_Scopus.pdf; http://irep.iium.edu.my/93472/; https://www.mdpi.com/1996-1073/14/21/7254/pdf
• Institution: Universiti Islam Antarabangsa Malaysia

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and support vector regressor (SVR). The CCPP energy output data was collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE) and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.