(From the explanation in the dataset source website: https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant) A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST) and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is collected from and has effect on the Steam Turbine, the other three of the ambient variables affect the GT performance.
Our regression task is to predict the energy output from four predictors: temperature, exhaust vacuum, ambient pressure, and relative humidity. More specifically, with several regression models (across parametric, non-parametric, and additive models), we perform model selection and verification of significance tests via statistical methods like cross-validation, forward stepwise regression, the lasso, and bootstrapping. We use the R programming language for this work.
Some notes:
- Due to rendering issues, the LaTeX source codes, instead of the neat math expressions, are displayed.
- The "Smoothing_matrix.RData" file in Section 7.2 is not uploaded due to its large size.
[Dataset Citation]
- Pınar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615
- Heysem Kaya, Pınar Tüfekci , Sadık Fikret Gürgen: Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai)