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This project uses machine learning to predict the electrical energy output of a power plant. By analyzing environmental variables, a Random Forest model was identified as the most accurate. Ambient temperature is the primary driver of efficiency in this regression task.

Project topic:
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/Combine..., 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)

Note: Dataset link not pasted.