<![CDATA[Cogeneration Gas turbines are the main equipment in the electrical grid system. To meet the load needs on the grid, power plants have several gas turbine units, with the same or different capacities. The load adjustment for each gas turbine unit is carried out by numerical calculation based on load needs, operating parameters, fuel consumption and steam production. In Fact, the recommended value of the numerical calculation is always above the turbine gas operation, resulting in inefficient fuel consumption. This research reformaltes a gas turbine dispatch problem into a data-driven optimization task. Researcher develop an Artificial Neural Network (ANN) on Multi Layer Preceptron (MLP) model using parameter data from 2024 with filters baseload-efficient condition. The Model produces unit capability rankings and validated within <2% error. Compared to dispatcher recommendations, average deviation ~7% with the model, enabling measurable fuel saving and increased steam production.]]>
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