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Power Plant O&M · model

Power Output Prediction

Predict electrical output from ambient variables

What does this project try to do?

Predict electrical output from ambient variables Open the Jupyter notebook, inspect features, train a regression baseline and evaluate prediction errors.

How does it work?

Open the Jupyter notebook, inspect features, train a regression baseline and evaluate prediction errors.

What will you learn?

Understand regression on publicly referenced combined-cycle data or local samples.

How to run this project

Option A — Google Colab (recommended for beginners)

  1. Click Download Notebook (.ipynb) below. The file should download instead of displaying raw JSON.
  2. Open Google Colab.
  3. Choose File → Upload notebook → Browse and select the downloaded .ipynb.
  4. Run any dependency-install cell first, if present.
  5. Choose Runtime → Run all, or run cells individually with ▶.
  6. Review tables, charts and evaluation metrics. If a compatible real dataset is used, upload it to Colab and update the notebook file path.

Option B — Run on your computer

  1. Install Python 3 and enable Add Python to PATH.
  2. Open Command Prompt and run pip install jupyterlab pandas numpy scikit-learn matplotlib openpyxl.
  3. Open the folder containing the downloaded notebook.
  4. Run jupyter lab.
  5. Open the .ipynb file and choose Run → Run All Cells.

Open, run or download

These projects run locally in Python, Jupyter or a browser. There is no hosted execution server; “Run” means opening a browser demo where provided or following the local setup steps.

Download Notebook (.ipynb)
Educational demonstration only. Bundled sample data may be synthetic. Results have not been independently validated for industrial or cybersecurity deployment. Do not upload confidential project data.