European commissioner for the Internal Market, Thierry Breton, told Le Journal du Dimanche in January 2022, “Existing nuclear plants alone will need 50 billion euros of investment from now until 2030. And new generation ones will need 500 billion”. This paper considers whether these values are realistic. Further, it asks whether these investments would yield an internationally competitive European nuclear power infrastructure given that the nuclear power industries in the Organization for Economic Cooperation and Development member countries have lost global nuclear market share to Russian and Chinese firms since 1995.The paper investigates whether the European nuclear industry even with massive investment can compete with the Chinese nuclear industries. It concludes that the European (in particular, the French) nuclear power industry will be unlikely to be cost competitive with the Chinese nuclear power industry unless financing and new plant orders are immediately forthcoming. To achieve carbon neutrality, the issue becomes whether European Union countries can afford indigenous nuclear technologies or will need to import nuclear power plants from Asia.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.