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 tempera...