The efficient use of a system is enabled with the life performance estimations. Thus, the effective use of underground resources is realized especially natural gas. Based on this, life performance models were generated to aim of improving the efficient use of energy for a combined cycle power plant (CCPP) of 243 MW installed in Izmir, Turkey by using fuzzy logic (FL) and artificial neural network (ANN) in this study. Therefore, output power estimations were carried out. Depending on the developed models, an estimation of the energy that the CCPP can produce and provide to the interconnected system in the following years has been made. According to the obtained results, the error prediction rates of FL and ANN models were determined. It was found that while the energy relative error estimation value that can be produced between the years calculated in modeling using FL varies between 0.59% and 3.54%, this value was found to vary between 0.001% and 0.84% in modeling using ANN. This result shows that the ANN model is more suitable for the life performance estimations of such a non-linear system.

系统的有效使用是通过寿命性能估计实现的。因此，实现了地下资源的有效利用，尤其是天然气。在此基础上，通过使用模糊逻辑 (FL) 和人工神经网络 (ANN) 生成寿命性能模型，旨在提高安装在土耳其伊兹密尔的 243 MW 联合循环发电厂 (CCPP) 的能源利用效率。这项研究。因此，进行了输出功率估计。根据开发的模型，对 CCPP 在接下来的几年中可以生产和提供给互连系统的能量进行了估计。根据得到的结果，确定了FL和ANN模型的误差预测率。发现虽然在使用 FL 建模时计算的年份之间可以产生的能源相对误差估计值在 0.59% 和 3.54% 之间变化，但在使用 ANN 建模时发现该值在 0.001% 和 0.84% 之间变化。该结果表明 ANN 模型更适合于这种非线性系统的寿命性能估计。