This paper introduces a novel Artificial Neural Network(ANN)-driven methodology for the techno-economic assessment(TEA)of Micro Gas Turbines(MGT)in energy applications,addressing the limitations of traditional TEA app...This paper introduces a novel Artificial Neural Network(ANN)-driven methodology for the techno-economic assessment(TEA)of Micro Gas Turbines(MGT)in energy applications,addressing the limitations of traditional TEA approaches which often lack adaptability to dynamic market conditions and technological advancements.The developed ANN model,employing a multi-layer perceptron architecture,leverages advanced machine learning techniques to accurately predict key economic indicators such as Net Present Value(NPV),Internal Rate of Return(IRR),Payback Period(PBP),and Return on Investment(ROI).Analysis of over 450 MGT-related energy project profiles validates the model's efficacy,demonstrating high predictive accuracy with a Mean Squared Error(MSE)of 0.0005 and an R-squared value of 0.993.The model is further validated across key application areas for MGT's,including PV and Solar,Distributed Energy Generation(DEG)and Hydrogen-Natural Gas blended systems for microgrid applications,showcasing its potential to enhance decision-making for energy investments.This approach not only streamlines the economic assessment process,reducing time and effort significantly,but also enhances decision-making for stakeholders by providing rapid,real-time economic analyses.The integration of ANN into MGT TEA sets a new standard for conducting techno-economic evaluations,potentially transforming energy system optimization practices.展开更多
文摘This paper introduces a novel Artificial Neural Network(ANN)-driven methodology for the techno-economic assessment(TEA)of Micro Gas Turbines(MGT)in energy applications,addressing the limitations of traditional TEA approaches which often lack adaptability to dynamic market conditions and technological advancements.The developed ANN model,employing a multi-layer perceptron architecture,leverages advanced machine learning techniques to accurately predict key economic indicators such as Net Present Value(NPV),Internal Rate of Return(IRR),Payback Period(PBP),and Return on Investment(ROI).Analysis of over 450 MGT-related energy project profiles validates the model's efficacy,demonstrating high predictive accuracy with a Mean Squared Error(MSE)of 0.0005 and an R-squared value of 0.993.The model is further validated across key application areas for MGT's,including PV and Solar,Distributed Energy Generation(DEG)and Hydrogen-Natural Gas blended systems for microgrid applications,showcasing its potential to enhance decision-making for energy investments.This approach not only streamlines the economic assessment process,reducing time and effort significantly,but also enhances decision-making for stakeholders by providing rapid,real-time economic analyses.The integration of ANN into MGT TEA sets a new standard for conducting techno-economic evaluations,potentially transforming energy system optimization practices.
