Sunil Kumar Paidi

Intelligent Systems Architecture: Real-Time Data Retrieval and Recommendation Engines for Smart Energy Grids

Abstract:

As power systems move toward smart grid technologies, large volumes of data are generated from sensors, smart meters, and monitoring systems. The challenge is not only collecting this data but also using it to support real-time decision making. This talk presents an architecture that combines Java Spring Boot microservices, AI models, and Elasticsearch-based hybrid search to enable intelligent monitoring and recommendation systems for smart energy infrastructure. Hybrid search integrates traditional keyword search with vector-based semantic search to identify patterns and anomalies in operational data. The approach demonstrates how modern backend architectures and AI technologies can support more efficient and reliable energy systems.

Profile:

I am a Lead Software Engineer specializing in backend systems, distributed architectures, and AI-driven intelligent platforms and extensive experience building scalable applications using Java, Spring Boot microservices, and cloud-native technologies. Mainly focuses on integrating artificial intelligence with large-scale backend systems to enable intelligent data retrieval, real-time analytics, and recommendation engines. I have experience in designing and integrated microservices across multiple enterprise systems while improving system performance, scalability, and reliability