Sample Code

Ready-to-use Python tutorials for working with Enertel forecast data. These examples work with both API responses and data extracts provided during trials. Copy and paste the code into Jupyter notebooks or Python scripts to get started quickly.

Available Tutorials

Data Pipelines

Learn how to retrieve and process forecast data from the Enertel API:

• Inference Pipeline - Basic data retrieval and processing
• Dashboard Forecasts - Working with dashboard forecast data
• Latest Forecasts - Retrieving the most recent forecasts
• Bulk Data - Handling large datasets efficiently

Forecast Evaluation

Analyze and evaluate forecast performance:

• Explore - Basic exploration of forecast data
• Day-Ahead Evaluation - Evaluating day-ahead forecasts
• Hourly Evaluation - Analyzing hourly forecast accuracy

Benchmarks

Compare and benchmark different forecasting approaches:

• Overview - Introduction to benchmarking methodologies

Getting Started

1. Prerequisites: Ensure you have Python installed with pandas, requests, and matplotlib
2. API Access: Set up your API token following the API Documentation
3. Choose a Tutorial: Start with the data pipelines to understand basic data retrieval
4. Run the Code: Copy the examples into Jupyter notebooks or Python scripts

Support

If you encounter issues with any of the sample code, please contact our support team through the Enertel application.