The purpose of the study is to provide an analysis of the prediction model for Cycle Power Plant(CCPP) in what regards the net hourly electrical energy output from four independent variables. A central point of the work is to apply the linear regression method for identifying the hourly electric energy output at Combined Cycle Power Plant(CCPP). Initial data set was obtained from UCI Machine Learning Repository and consisted of 9,567 rows and 5 columns, organized as follows: Ambient Temperature (AT), Ambient Pressure (AP), Relative Humidity (RH), Exhaust Vacuum (EV), and net hourly electrical energy output (PE) of the plant.

From implementation standpoint, 1) R language was initially used, and 2) a customized application was specifically developed for this purpose. It provides visualization and analysis capabilities like identifying the outliers and removing them with ease before building the prediction model.

As the results proved, in the end all independent variables must be used in order to obtain an accurate prediction model for the hourly electrical energy output. The same steps were performed twice, first using only R language and second time using the aforementioned customized C# application. The final results have shown an accurate prediction model for CCPP.

Results:

Develop application that provide data analysis functionality and visualization model for the prediction model.

To view the full study, please register.