ohmech / coursera-ml-predict-logistic-regression-vertexai

AI Product Management Specialization | Coursera

coursera-ml-predict-logistic-regression-vertexai

AI Product Management Specialization | Coursera

In this project we will build a model to predict the electrical energy output of a Combined Cycle Power Plant (https://en.wikipedia.org/wiki/Combined_cycle_power_plant), which uses a combination of gas turbines, steam turbines, and heat recovery steam generators to generate power. We have a set of 9568 hourly average ambient environmental readings from sensors at the power plant which we will use in our model.

The columns in the data consist of hourly average ambient variables:

• Temperature (T) in the range 1.81°C to 37.11°C,
• Ambient Pressure (AP) in the range 992.89-1033.30 milibar,
• Relative Humidity (RH) in the range 25.56% to 100.16%
• Exhaust Vacuum (V) in the range 25.36-81.56 cm Hg
• Net hourly electrical energy output (PE) 420.26-495.76 MW (Target we are trying to predict)

Data source: 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)