Power Price Forecasting Project (Development Paused)

Introduction

This project aims to forecast power prices in Norway using machine learning techniques. The project demonstrates a good workflow for a data scientist, including data collection, cleaning, processing, model training, evaluation, and visualization.

Project Structure

• data/: Contains raw and processed data files.
• notebooks/: Jupyter notebooks documenting the process.
• src/: Source code for data collection, processing, and modeling.
• models/: Trained models and model-related files.
• README.md: Project documentation.
• .gitignore: Git ignore file.

Setup Instructions

Prerequisites

• Ubuntu on WSL2 (Windows 11)
• Python 3.8+
• Virtual Environment (venv)

Installation

1. Create and activate a virtual environment:

   python3 -m venv PPP_env
   source PPP_env/bin/activate

2. Clone the repository:

   git clone [email protected]:Jon-Bull/PowerPricePrediction.git
   cd PowerPricePrediction

3. Install the required packages:

   pip install -r requirements.txt

Data Collection

Data is collected from the following APIs:

• Power Prices: NVE API
• Weather Data: OpenWeatherMap, Yr (Norwegian Meteorological Institute)

Running the Project

1. Data Collection:

   python src/data_collection.py

2. Data Cleaning:

   python src/data_cleaning.py

3. Model Training:

   python src/model_training.py

4. Model Evaluation:

   python src/model_evaluation.py

Results

Results and visualizations can be found in the notebooks/ directory and the reports/ directory.

License

This project is licensed under the MIT License.