Modelling and Energy Optimization of a Thermal Power Plant Using a Multi-Layer Perception Regression Method

Abstract

This research paper studies a thermal power plant model with an Artificial Neural Network that contributes to the accuracy improvement of actual measurement data. Neural Networks process the paradigm of algebraic expressions, and their training occurs via a Feed-Forward Back Propagation algorithm implemented in a MATLAB environment. The applied training case in a thermal power plant in Paracha includes three different algorithms, the Levenberg-Marquadt, the Scaled Conjugate Gradient, and the Bayesian Regularization, considering less number of samples to achieve more reliable results. The outcome highlights Bayesian Regularization Networks’ superiority in accuracy and performance compared to Levenberg-Marquadt and the Scaled Conjugate Gradient. The regression analysis estimates the relationship between input-independent and output-dependent variables, forecasts the energetic data, and highlights the benefits of the Bayesian Regularization method in the energy sector.