  This paper presents the application of an adaptive neuro-fuzzy inference
system (ANFIS) to predict the generated electrical power in a combined cycle
power plant. The ANFIS architecture is implemented in MATLAB through a code
that utilizes a hybrid algorithm that combines gradient descent and the least
square estimator to train the network. The Model is verified by applying it to
approximate a nonlinear equation with three variables, the time series
Mackey-Glass equation and the ANFIS toolbox in MATLAB. Once its validity is
confirmed, ANFIS is implemented to forecast the generated electrical power by
the power plant. The ANFIS has three inputs: temperature, pressure, and
relative humidity. Each input is fuzzified by three Gaussian membership
functions. The first-order Sugeno type defuzzification approach is utilized to
evaluate a crisp output. Proposed ANFIS is cable of successfully predicting
power generation with extremely high accuracy and being much faster than
Toolbox, which makes it a promising tool for energy generation applications.
