Data Collection: FGRID’s AI model begins by gathering extensive datasets from the energy sector. 

Data Preprocessing: The collected data undergoes rigorous preprocessing to ensure its suitability for analysis. 

Model Selection: The AI model leverages advanced machine learning algorithms suitable for predictive analytics. 

Feature Engineering: Key to the AI model’s success is identifying the most influential features impacting energy prices and production. 

Training the Model: The AI model is trained on historical data, enabling it to learn, recognize patterns, and establish relationships within the data, crucial for accurate forecasting.

Model Testing and Validation: Rigorous testing and validation on a separate dataset are conducted to ensure the model’s accuracy and its ability to generalize predictions.

Integration with Blockchain: The AI model is seamlessly integrated into the FGRID’S blockchain infrastructure, allowing access to real-time data for continuous analysis and prediction.

Continuous Learning and Adaptation: Mechanisms for continuous learning are implemented, enabling the AI model to adapt its predictions based on new data, thereby maintaining high accuracy over time.

User Interface Development: We’re crafting a user-friendly interface that  seamless access to AI-driven forecasts and insights, enhancing decision-making. Additionally, we’re integrating this interface with virtual reality using Unreal Engine 5.3, that brings data to life, further enriching user engagement and understanding.

Security and Compliance: Paramount to the model’s deployment is adherence to strict data privacy laws and cybersecurity standards, ensuring the integrity and confidentiality of sensitive energy data.