An ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant

Thermofluid dynamics model of a combined cycle gas turbine, with energy and exergy analysis.

An industrial digital twin solution for Combined Cycle Power Plants (CCPP). This project leverages XGBoost machine learning models and mathematical optimization (SLSQP) to predict net electrical output and prescribe the optimal exhaust vacuum setpoint, maximizing thermal efficiency based on real-time environmental conditions.

This project demonstrates the implementation of an Artificial Neural Network (ANN) model to predict the net hourly electrical energy output of a Combined Cycle Power Plant (CCPP).

An Implementation of the Gradient Descent Algorithm on the 🏭Combined Cycle Power Plant DataSet🏭.

End-to-end machine learning workflow on the Combined Cycle Power Plant dataset: data cleaning, EDA, outlier removal, feature engineering, class balancing, and model evaluation for regression and classification. Includes code, visualizations and best practices in a single Jupyter notebook.