Project-Repo

Study on factors affecting power output of a combined cycle power plant and prediction based on data.

• A combined cycle power plant is one of the most efficient power plants that uses both gas and steam turbines to produce 50% more electricity than what a traditional simple cycle plant produces.

• The main aim of this paper is to predict the output of the combined cycle power plant using several machine learning algorithms.

• Predicting the full load electric power is extremely instrumental in order to maximize the profit from the available megawatt hours.

• The base load operation of this power plant is affected by four main factors:

  • Ambient temperature
  • Atmospheric pressure
  • Relative humidity
  • Exhaust steam pressure

• The power output, which is influenced by these factors, is considered as the target variable.

• This paper consists of a detailed study of the data available and assesses which machine learning algorithm works the best in examining the factors and predicting the output.

• The most accurate machine learning algorithm is found using various accuracy metrics.

  The power output, influenced by these factors, is considered the target variable. This project consists of a detailed study of the available data and assesses which machine learning algorithm works the best in examining these factors and predicting the output.

Machine Learning Models Used

• Linear Regression
• Polynomial Regression
• Decision Tree Regression
• Random Forest Regression

Metrics for Evaluation

The accuracy of each machine learning algorithm is evaluated using the following metrics:

• R2 Score
• Mean Absolute Error
• Mean Squared Error
• Root Mean Squared Error

Linear Regression Accuracy Metrics

Metric	Value
R2 Score	0.9278034642596562
Mean Absolute Error	3.6374586763847
Mean Squared Error	20.95100810799155
Root Mean Squared Error	4.577227119992141

Polynomial Regression Accuracy Metrics

Metric	Value
R2 Score	0.9416201631798347
Mean Absolute Error	3.2051238883549265
Mean Squared Error	16.941483715527134
Root Mean Squared Error	4.116003366802211

Decision Tree Regression Accuracy Metrics

Metric	Value
R2 Score	0.8593733120875664
Mean Absolute Error	5.106282749531314
Mean Squared Error	40.80903395766401
Root Mean Squared Error	6.388194890394627

Random Forest Regression Accuracy Metrics

Metric	Value
R2 Score	0.9395235633502752
Mean Absolute Error	3.2388779030504384
Mean Squared Error	17.549904598577477
Root Mean Squared Error	4.189260626718929

Files

• Decision Tree.ipynb: Jupyter notebook for Decision Tree Regression.
• Folds5x2_pp.csv: Dataset used for training and testing the models.
• Linear Regression.ipynb: Jupyter notebook for Linear Regression.
• Polynomial Regression.ipynb: Jupyter notebook for Polynomial Regression.
• Project Report.pdf: Detailed report of the project.
• Project Summary.pdf: Summary of the project.
• README.md: This file.
• Random Forest.ipynb: Jupyter notebook for Random Forest Regression.
• analysis-and-prediction-of-power.ipynb: Jupyter notebook for data preparation.
• final_prepared.xlsx: Excel file containing prepared data.