Tam yük elektrik tahmini bir temel yükle çalışanın güç çıkışı kombine çevrim santrali kullanımı yapay sinir ağları gerilemesi

A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant. The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant. The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables using ANN algorithm which used to obtain regression model that predicts electrical output power (EP) of combined cycle power plant based on 4 inputs. The use of the ANN models indicates additionally higher execution for bigger preparing dataset measure; at the other hand, it demonstrates extraordinary impact of bigger number of factors as the provided information. Dataset is obtained from an open online source. The work shows and explains the stochastic behavior of the regression neural, experiments the effect of number of neurons of the hidden layers. It shows also higher performance for larger training dataset size; at the other hand, it shows different effect of larger number of variables as input. The results we obtained, we can deduce that the algorithm that has given the best performance would be ANN with a peak accuracy of 99% in almost all of the three variations with 10, 50 and 100 nodes.