Prediction of energy generated from composite cycle power plant in smart cities

Abstract

In smart cities, it is significant to predict the energy generated from composite cycle power plants, in order to reduction the efforts and to extreme profit from the megawatt hours during the operation, which is consider from the important topics in mechanical engineering. The most significant component of a power plant with a composite cycle is the gas turbine, which generates the entire electrical energy via a fuel and distributes it to homes, schools and other institutions around the cities. In this paper artificial neural networks, regression machine learning with decision tree method is utilized to develop a model that can be able to predictive the estimate of electrical energy output of a composite cycle power plant. The basis load functioning of plant is impacted through four key characteristics, such as surroundings temperature, relative humidity, pressure of atmospheric, and generated steam pressure, which are utilized in the dataset as input variables. These variables have an impact on electrical energy output, which is the desired variable. The input and target variables are included in the dataset, obtained from an open online source. The decision tree algorithm produced the best results, with a mean absolute error of (0.009) and a root mean square error of (0.022).