Gabriele Piantadosi

Researcher at ENEA, Energy and Data Science Laboratory (TERIN-SSI-EDS). His activity lies at the intersection of artificial intelligence, embedded systems and renewable energy, with a focus on monitoring, predictive diagnostics and forecasting of photovoltaic and wind plants in distributed and minimally supervised scenarios. His research addresses federated and split learning for anomaly detection across multi-plant fleets, Transformer-based forecasting of solar generation, the integration of Earth Observation satellite data into forecasting pipelines, and edge AI for on-device intelligence in low-power sensor networks. The expertise developed on terrestrial energy assets operating with limited human supervision represents a body of methodological paradigms directly applicable to energy systems for space applications. He also teaches Machine Learning & Big Data at the University of Naples Federico II. Areas of contribution on Space and Energy The Intelligent Layer of Space Energy Systems: •\tAI-based predictive diagnostics and Remaining Useful Life (RUL) estimation of power subsystems and critical components in autonomous operation •\tEdge AI for real-time optimal balancing of generation, loads and storage under bandwidth and compute constraints •\tFederated and split learning for satellite constellations and distributed clusters of energy assets •\tEnergy-aware forecasting integrated into the control loops of isolated microgrids •\tEarth Observation products and computer-vision / machine-learning pipelines supporting O&M, diagnostics and generation forecasting of terrestrial renewable plants •\tSatellite data and AI-driven geospatial analytics supporting resource assessment, siting, planning and the development of new capacity in the energy value chain