PureChain web-based energy predictor with federated learning Dirichlet for real-time energy consumption forecasting

Area of Science:

• Smart Grid Technology
• Artificial Intelligence
• Cybersecurity

Background:

• Accurate energy forecasting is vital for smart grids but challenged by data heterogeneity and privacy concerns.
• Existing federated learning methods struggle with non-IID data and slow blockchain consensus mechanisms.
• Real-time, secure, and privacy-preserving solutions are needed for effective smart grid operations.

Purpose of the Study:

• To introduce PureChain, a federated learning framework integrated with a permissioned blockchain for smart grid energy forecasting.
• To address limitations of existing approaches regarding data heterogeneity, real-time performance, and security.
• To validate the framework's effectiveness in privacy-sensitive smart grid environments.

Main Methods:

• Developed PureChain framework combining federated averaging, Dirichlet partitioning, LSTM forecasting, and a permissioned blockchain (PoAα).
• Implemented a partitioning strategy to enhance training stability under extreme non-IID conditions.
• Utilized a permissioned blockchain with Proof-of-Authority and Association (PoAα) consensus for low-latency operations.

Main Results:

• The Dirichlet partitioning strategy improved training stability under extreme non-IID data.
• The PoAα consensus achieved 2.0s transaction latency and 20.88 TPS, outperforming Hyperledger Fabric and Quorum.
• LSTM forecasting achieved an average R² of 0.9184 across clients under high data heterogeneity.
• Smart contract security assessment yielded a high threat score (98.5/100), indicating robust security.

Conclusions:

• PureChain effectively integrates federated learning, forecasting, and blockchain for privacy-preserving smart grid energy consumption.
• The framework demonstrates superior performance and security for real-time, heterogeneous smart grid data.
• PureChain offers a viable solution for privacy-sensitive smart grid deployments requiring accurate energy forecasting.