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| title: Electricity Grid Model | |
| emoji: ⚡ | |
| colorFrom: yellow | |
| colorTo: red | |
| sdk: gradio | |
| app_port: 7860 | |
| app_file: app.py | |
| pinned: false | |
| # Electricity Grid Pricing Prediction Model | |
| Machine learning model for forecasting Australian electricity spot prices. | |
| ## Overview | |
| This repository contains the implementation of a pricing prediction model deployed on Hugging Face. It generates short-term forecasts for electricity prices based on historical and real-time data. | |
| ## Features | |
| - Predicts spot prices at: | |
| - 5 minutes | |
| - 15 minutes | |
| - 30 minutes | |
| - Integrated with external APIs for live inference | |
| - Deployable via Hugging Face Spaces | |
| ## Model Deployment | |
| The model is hosted on Hugging Face and exposed via an API endpoint for inference. | |
| ## Input | |
| - Time-series electricity data | |
| - Market indicators | |
| ## Output | |
| - Predicted spot prices for defined time horizons | |
| ## Integration | |
| This model integrates with: | |
| - Electricity Grid API (data input) | |
| - AWS Lambda (automated testing and triggering) | |
| ## Usage | |
| The model can be accessed via API calls or integrated into downstream applications such as dashboards or trading tools. | |
| ## Notes | |
| - Ensure input data is preprocessed consistently | |
| - Model performance depends on data freshness | |