Risks
- Running ML models on Raspberry Pi could cause performance limitations due to limited computing power. To address this, I will optimize models for efficiency and consider offloading computations to an external server.
- Additionally, compatibility between ML frameworks and Home Assistant might pose challenges.I will validate API integrations using Postman to address this.
Changes
No major changes in implementation/design yet. After trying to deploy the ML model on RPi, if the RPi has limited storage/processing power, will deploy the ML forecasting on a computer.
Progress
- Automation setup with Home Assistant.
- Initial implementation of optimization models.
- Model training and prediction framework.
- CSV data storage and Docker integration.
- Nordpool grid price retrieval setup.