Combined Cycle Power Plant – Energy Output Prediction

Описание: Great! Let’s break down your Combined Cycle Power Plant energy output prediction project into a clear, step-by-step plan you can follow in Python. I’ll guide you on the machine learning approach, features, models, validation, and evaluation, so you have a complete roadmap.

1️⃣ Define the ML Approach and Output Metric
Problem Type

We want to predict a continuous variable: Net hourly electrical energy output (PE in MW).

✅ This is a regression problem.

Output Metric

For regression, common metrics include:

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)

R² score (coefficient of determination)

💡 Recommended for this project:

RMSE: Gives error in the same units as the target (MW).

R² score: Measures how well the model explains variance in the data.

2️⃣ Determine Features and Target
Features (Inputs)

Temperature (T)

Ambient Pressure (AP)

Relative Humidity (RH)

Exhaust Vacuum (V)

Target (Output)

Net hourly electrical energy output (PE)

3️⃣ Possible Algorithms

For regression, consider:

Algorithm Notes
Linear Regression Simple baseline model
Decision Tree Regressor Captures non-linear relationships
Random Forest Regressor Ensemble, reduces overfitting
Gradient Boosting Regressor Powerful, works well for tabular data
Support Vector Regressor (SVR) Good for non-linear patterns
Neural Networks (MLPRegressor) For complex patterns if dataset is large

💡 Start with Linear Regression as a baseline and Random Forest / Gradient Boosting for better accuracy.