The distributed model identification framework presented in this paper has applications beyond power systems in various domains where decentralized decision-making and real-time adaptation are crucial. Some potential applications include:
Autonomous Vehicles: Distributed model identification can be utilized in autonomous vehicle networks to improve decision-making and control algorithms based on real-time sensor data. Each vehicle can update its model parameters autonomously, leading to more efficient and adaptive driving behavior.
Healthcare Systems: In healthcare, distributed model identification can enhance patient monitoring and treatment optimization by allowing medical devices and systems to learn and adapt to individual patient data in real-time. This can lead to personalized and responsive healthcare solutions.
Financial Markets: Distributed model identification can be applied in financial markets for risk assessment, fraud detection, and algorithmic trading. Agents can update their models based on market data to make informed decisions and optimize trading strategies dynamically.
Environmental Monitoring: Environmental monitoring networks can benefit from distributed model identification to analyze and predict changes in environmental parameters. By updating models based on distributed sensor data, these systems can improve forecasting accuracy and response strategies for environmental events.