Energy Informatics • SMART GRIDS • Energy Forecasting • Energy Management
Energy Informatics specialist with a PhD, focused on predictive energy intelligence, combining forecasting, edge computing, and digital twins for real-time decision-making in smart grids and intermittent energy environments.
An overview of my focus areas and working style.
Research in Energy Informatics and AI-driven energy systems, focusing on time-series forecasting, energy-aware decision support, and predictive intelligence. My work addresses both smart grids and intermittent energy systems, combining data analytics, power-system modeling, and intelligent control to improve reliability, efficiency, and sustainability under uncertainty.
Engineering of end-to-end machine learning and energy-informatics systems for power and energy applications. This includes data integration, feature engineering, model development and evaluation, and deployment-oriented system design, with particular attention to real-time operation, scalability, and edge and embedded constraints.
Academic–industry collaboration in Energy Informatics, energy management, and digital twin technologies for power systems. This includes active involvement in the ReTW initiative, contributing to the development of an educational digital twin platform for smart grid analytics, energy management, and decision support, bridging theoretical research with real-world applications.
