Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model

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Journal of Power and Energy Engineering

ISSN Print: 2327-588X
ISSN Online: 2327-5901
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"Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model"
written by Elkhawad Ali Elfaki, Ahmed Hassan Ahmed,
published by Journal of Power and Energy Engineering, Vol.6 No.12, 2018

has been cited by the following article(s):

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[15]	Life performance prediction of natural gas combined cycle power plant with intelligent algorithms
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[16]	Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms
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[20]	Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm
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[22]	Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine
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[14]	Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm Measurement, 2022 DOI:10.1016/j.measurement.2022.111405

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[17]	Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3153720

[18]	Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm Measurement, 2022 DOI:10.1016/j.measurement.2022.111405

[19]	Applicability of correlational data-mining to small-scale turbojet performance map generation International Journal of Turbo & Jet-Engines, 2021 DOI:10.1515/tjj-2021-0062

[20]	Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines International Journal of Energy Research, 2021 DOI:10.1002/er.6093

[21]	Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method Sensors, 2021 DOI:10.3390/s21041022

[22]	Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms Energies, 2021 DOI:10.3390/en14217254

[23]	Life performance prediction of natural gas combined cycle power plant with intelligent algorithms Sustainable Energy Technologies and Assessments, 2021 DOI:10.1016/j.seta.2021.101398

[24]	Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm-Based Paradigm Wireless Communications and Mobile Computing, 2021 DOI:10.1155/2021/9966395

[25]	Forecasting the energy output from a combined cycle thermal power plant using deep learning models Case Studies in Thermal Engineering, 2021 DOI:10.1016/j.csite.2021.101693

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[27]	Life performance prediction of natural gas combined cycle power plant with intelligent algorithms Sustainable Energy Technologies and Assessments, 2021 DOI:10.1016/j.seta.2021.101398

[28]	Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine IEEE Access, 2020 DOI:10.1109/ACCESS.2020.2976452

[29]	Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation Energies, 2020 DOI:10.3390/en13215619

[30]	Predicting the power of a combined cycle power plant using machine learning methods 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), 2020 DOI:10.1109/CCCI49893.2020.9256742

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	+86 18163351462(WhatsApp)
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