On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm-Reference-Cited by-同舟云学术

On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm

Published:2024-07 Issue: Volume:133 Page:108213
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Anđelić Nikola^ORCID,Lorencin Ivan,Mrzljak Vedran,Car Zlatan

Funder

University of Rijeka

Publisher

Elsevier BV

Reference54 articles.

1. Modeling, simulation and optimization of power plant energy sustainability for IoT enabled smart cities empowered with deep extreme learning machine;Abbas;IEEE Access,2020

2. Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms;Afzal;Energies,2021

3. Continuous conditional random field model for predicting the electrical load of a combined cycle power plant;Ahn;Ind. Eng. Manag. Syst.,2016

4. Prediction of hourly generated electric power using artificial neural network for combined cycle power plant;Akdemir;Int. J. Electr. Energy,2016

5. Analytical closed-form model for predicting the power and efficiency of stirling engines based on a comprehensive numerical model and the genetic programming;Babaelahi;Energy,2016

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of electricity load generated by Combined Cycle Power Plants using integration of machine learning methods and HGS algorithm;Computers and Electrical Engineering;2024-12

2. Geotechnical risk modeling using an explainable transfer learning model incorporating physical guidance;Engineering Applications of Artificial Intelligence;2024-11

3. Co-guided Dual-channel Graph Neural Networks for the prediction of compound–protein interaction;Applied Soft Computing;2024-09