Artificial Neural Network (ANN) driven Techno-Economic Predictions for Micro Gas Turbines (MGT) based Energy Applications

Abstract

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.