Rafael Natalio Fontana Crespo - Sustainable Development - Young Scientist Award
Young Scientist Award - Italy
AUTHOR PROFILE
ORCID
RAFAEL NATALIO FONTANA CRESPO ๐
Rafael Natalio Fontana Crespo is a distinguished researcher in the field of power systems and renewable energy forecasting. His expertise lies in applying machine learning techniques to optimize grid power forecasting and improve the reliability of renewable energy systems. He is particularly known for his innovative work in wave energy converters, making him a leader in the intersection of artificial intelligence and sustainable energy technologies.
RESEARCH IN WAVE ENERGY ๐
Rafael's research primarily focuses on short-term grid power forecasting, where he leverages machine learning techniques to enhance the prediction accuracy of wave energy converters. His recent comparative analysis of these techniques has highlighted new methods for improving uncertainty analysis in energy forecasting, which is crucial for integrating renewable sources into the grid.
EXPERT IN ARTIFICIAL INTELLIGENCE ๐ค
With a strong foundation in engineering applications of artificial intelligence, Rafael has made significant contributions to the field. His work on LSTM (Long Short-Term Memory) models for grid power forecasting provides novel insights into how AI can be applied to improve the stability and efficiency of energy systems. His research paves the way for smarter, AI-driven energy management solutions.
INNOVATIONS IN ADDITIVE MANUFACTURING ๐ ๏ธ
Rafael's contributions extend beyond energy systems to manufacturing technologies. He played a key role in developing a distributed software platform for additive manufacturing, showcased at the IEEE International Conference on Emerging Technologies and Factory Automation. This platform aims to streamline the manufacturing process, pushing forward the boundaries of Industry 4.0.
PUBLICATIONS AND IMPACT ๐
Rafael has authored several influential papers in prestigious journals and conferences. His work on machine learning and energy systems has been widely cited, including his 2024 paper on grid power forecasting published in the Engineering Applications of Artificial Intelligence journal. He continues to influence the scientific community with his cutting-edge research in AI applications.
CONFERENCE CONTRIBUTIONS ๐
Rafael regularly presents his research at international conferences, such as the IEEE Annual Computers, Software, and Applications Conference (COMPSAC). His presentations on wave energy forecasting and AI-based solutions for energy grid management have garnered attention, solidifying his reputation as a forward-thinking engineer and researcher in global platforms.
FUTURE-ORIENTED RESEARCH ๐ง
With a clear vision for the future, Rafael aims to continue advancing AI-driven solutions for renewable energy systems. His ongoing work explores the integration of artificial intelligence in real-time energy management, making energy grids more adaptive, resilient, and capable of supporting large-scale renewable energy adoption.
NOTABLE PUBLICATION
Title: A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters
Authors: Rafael Natalio Fontana Crespo, Alessandro Aliberti, Lorenzo Bottaccioli, Edoardo Pasta, Sergej Antonello Sirigu, Enrico Macii, Giuliana Mattiazzo, Edoardo Patti
Year: 2024
Journal: Engineering Applications of Artificial Intelligence
Title: A Distributed Software Platform for Additive Manufacturing
Authors: Rafael Natalio Fontana Crespo, Davide Cannizzaro, Lorenzo Bottaccioli, Enrico Macii, Edoardo Patti, Santa Di Cataldo
Year: 2023
Conference: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
Title: LSTM for Grid Power Forecasting in Short-Term from Wave Energy Converters
Authors: Rafael Natalio Fontana Crespo, Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Giorgio Fighera, Edoardo Patti
Year: 2023
Conference: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)