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    PREDICTION OF THE POWER OUTPUT OF A POWER GENERATION GAS TURBINE USING ARTIFICIAL NEURAL NETWORK (ANN) APPROACH -CASE STUDY LIBYA
    (2020-11-13) Emdalel, Ali Salem Mohamed
    Today, regression artificial neural networks (ANN) have found their way into simulating different systems possessing advanced dimensions and having different outputs and inputs. This study attempts to forecast the energy output related to the gas turbines (GT) at the Al hawamid Power Plant in Libya by means of an ANN approach. The stated power station is exposed to a number of variables, which will be employed in terms of the input to obtain the power output generated by the turbines. To this end, we will use an ANN model for the prediction of this output, not to mention a Neural Fitting tool (nftool) to assist us in solving the related fitting issues by means of a two-dual-level feed-forward system developed based on the Levenberg-Marquardt Algorithm (LMA). Our results show that the stated approach is an ideal back propagation algorithm at 10 neurons related to our subject turbines. Also, the most suitable fit based on the employed ANN stands at the R2 values of 0.9999, 0.9999, 0.972, and 0.999, respectively for the tested turbines. Lastly, it can be stated that the suggested ANN can be applied at a sound and acceptable level in place of mechanism to forecast the power output of a given GT. To this end, hypothetical structures using the approach play an important role so as to come up with an ideal process and the best outcome.