Prof., Dr. M. Thameur CHAIBI, Director of Research at INRGREF, Tunisia
Biography: Prof Chaibi has worked for nearly three decades in science, technology, higher education, and research. He has held significant roles, including serving the African Union Commission (AUC), GIZ, and the National Research Institute for Agricultural Engineering, Water, and Forestry (INRGREF). With a PhD in Agriculture Engineering and Climate Technologies from SLU (Sweden), he has authored/co-authored over 100 papers and contributed to 7 book chapters. Prof Chaibi is a member of various editorial boards and has coordinated several funded research projects. He is follow of the Academy of Sciences for the Developing World (TWAS), the African Academy of Sciences (AAS) and the Islamic World Academy of Sciences (IAS), The French Water Academy and has served on important international committees and advisory groups.
Prof. Doan PHAM MINH, Ecole Nationale Supérieure des Mines d'Albi, France
Biography: Doan PHAM MINH received his PhD degree in chemistry at the University Lyon 1, France in 2006. After post-doc experiences at French Institute of Petroleum and IRCELYON (France), in 2009, he has joint IMT Mines Albi as assistant professor, affected at the RAPSODEE research center. Since 2011, he is associate professor at IMT Mines Albi where he has led (2016-2021) a research team composed of 5 permanent staffs and around 15 PhD students and post-doc. Currently, he is Full Professor and Deputy Director of the RAPSODEE research center (around 100 persons). In parallel with teaching activities, his research is focussed on the development of performing functional materials applied in the energy field. He mostly works on new catalysts and catalytic process engineering for the valorisation of biomass, bio-wastes and industrial coproducts into energy carriers, e.g. syngas, hydrogen, methane, alternative fuels etc., as well as on refractory ceramics applied in high-temperature sensible thermal energy storage. He is principal investigator (PI) and co-PI of more than 40 research projects, and is supervisor and co-supervisor of 26 PhD students, 13 post-docs and research engineers and 27 internships. He is currently editorial board member of Waste and Biomass Valorization (Springer), and co-organizer of the Summer School in catalysis which is held every two years since 2019.
Prof. Daniel Ambach, Digital Business University of Applied Sciences
Biography: Prof. Dr. Daniel Ambach, holds the chair of Data Science and Computational Statistics, at the Digital Business University of Applied Sciences (DBU). His academic and professional journey showcases his expertise in practical and theoretical aspects of data science. His career spans various roles in academia and industry, reflecting his commitment to applying data science in real-world scenarios. He has served as Principal Data Scientist at Deutsche Bahn AG and held teaching positions at HTW Berlin and HWR Berlin. His primary research interest lies in renewable energy forecasting, particularly in wind energy prediction. His contributions to academia are highlighted by his numerous publications in peer-reviewed journals, focusing on topics such as wind speed forecasting and the application of statistical methods in renewable energy.
Speech title“Recent Advances and Comparative Analysis in Short to Medium-Term Wind Speed Prediction”
Abstract-In the dynamic field of wind speed prediction,
significant improvements have been achieved in developing robust
prediction models suitable for short to medium-term forecasts. This
presentation will provide an overview of recent developments, shedding
light on the advancements in various forecasting methods. We will focus
on developments and innovations within the field of deep learning,
statistical time series approaches and other ensemble prediction models.
These methods have specific advantages while used for meteorological
predictions, each offering unique strengths in handling the complexity
and variability of wind speed data.
Moreover, we will present preliminary results from a comprehensive comparison study. This study aims to evaluate and the performance, applicability and the effectiveness of the aforementioned modelling approaches. The spectrum of wind speed prediction approaches includes e.g.: traditional time series methods, sophisticated deep learning and stacked ensemble approaches. Hence, the use of periodic spline regression is highlighted, demonstrating its utility in capturing cyclical patterns in wind speed data.
Through this comparative analysis, the presentation aims to offer a clearer understanding of the current capabilities and limitations of different wind speed forecasting models and methodologies. It outlines future research directions, emphasizing the importance of combining diverse methodologies to enhance prediction accuracy and reliability in the field of meteorological forecasting.
Dr. Nguyen Thuy Chung, Hanoi University of Science and Technology, Vietnam
Biography: Dr. Nguyen Thuy Chung is a lecturer of School of Environmental Science and Technology, Hanoi University of Science and Technology, Hanoi, Vietnam. She obtained his Ph.D on the “Pollutants in road-deposited sediments: characteristics, mobility, bioavailability and remediation” from The University of Technology Sydney in 2015. Since 2009, she has worked at the Hanoi University of Science and Technology. Her research interests include wastewater treatment technology, low cost absorbents, heavy metal removal, road deposited sediment, risk management. She already published more than 7 ISI papers on her research recently. Recently, she has participated some activities with “Vietnam Association of Urban Environment and Industrial Park”.
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