Combined Cycle Power Plant
A combined-cycle power plant uses both gas and a steam turbine together to produce up to 50 percent more electricity from the same fuel than a traditional simple-cycle plant. The waste heat from the gas turbine is routed to the nearby steam turbine, which generates extra power.
All the UN Member States have to submit a report on the combined cycle power plant to the United Nations. The Power Plant officials of Mexico are devising a way to predict the net hourly electrical energy output (PE) of the plant. You are appointed as the chief for this operation. Create a Machine Learning Model to solve this problem efficiently.
Submissions are evaluated using RMSE Value. How do we do it?
Once you generate and submit the target variable predictions on evaluation dataset, your submissions will be compared with the true values of the target variable.
The True or Actual values of the target variable are hidden on the DPhi Practice platform so that we can evaluate your model's performance on unseen data. Finally, an RMSE Value for your model will be generated and displayed
About the dataset
The data points were collected from a Combined Cycle Power Plant over 6 years (2006-2011) when the power plant was set to work with a full load. Features consist of hourly average ambient variables - Ambient Temperature (AT), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (PE) of the plant.
To load the dataset in your jupyter notebook, use the below command:
import pandas as pd ccpp_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/CCPP/Training_set_ccpp.csv')
The dataset contains 9568 observations with 5 features/variables.
- AT: Ambient Temperature - Numerical (values ranges from 1.81°C to 37.11°C)
- AP: Ambient Pressure - Numerical (values ranges from 992.89 to 1033.30 milibar)
- RH: Relative Humidity - Numerical (values ranges from 25.56% to 100.16%)
- EV: Exhaust Vacuum - Numerical (values ranges from 25.36-81.56 cm Hg)
- PE: Net hourly electrical energy output - Numerical (values ranges from 420.26-495.76 MW)
Load the evaluation dataset (name it as 'CCPP_eval'). You can load the data using the below command.
CCPP_eval = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/CCPP/Testing_set_ccpp.csv')
This dataset is downloaded from the UCI Machine Learning Repository - https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant
To participate in this challenge either you have to create a team of atleast members or join some team