Wind Energy and Weather Forecasting

George Xydis had the opportunity to work in the wind sector together with developers, utilities, constructors, universities, and research institutes. He used to work as a Wind Projects Development Coordinator at Iberdrola Renewables, as a Wind Project Developer at Vector Hellenic Windfarms S.A., and as a Researcher at the Center for Electric Power and Energy, Department of Electrical Engineering at the Technical University of Denmark. He has been since February 2017 with the Center for Energy Technologies, Aarhus University as an Associate Professor and since January 2023 as a Full Professor. George was one of the co-founders of VerdeCube and worked as an adjunct faculty member at Johns Hopkins University in Energy Policy and Climate program, teaching 425.624.81 – Wind Energy: Science, Technology and Policy. He has been since January 2024 with the University of the Peloponnese, Dept. of Mechanical Engineering. He used to collaborate, as a freelancer, with institutes, universities, and SMEs. George’s research interests include energy planning, wind resource assessment, technoeconomics of renewable energy systems, energy demand and plant factories. He has co-authored more than 140 publications in international journals, more than 50 in international conferences, books, and book chapters, and serves as an Associate Editor in Frontiers in Energy Systems & Policy, Journal of Business Analytics, and Energy & Environment. Since May 2024, he has served as the Co-Editor in Chief of Cambridge Prisms: Energy Transitions, a scholarly journal that Cambridge University Press launched. He holds a Ph.D. from the National Technical University of Athens, and a degree in Mechanical Engineering from the Aristotle University of Thessaloniki, Greece.

• 1. Introduction 2. Machine Learning for Improving Non-conformance Data Management in the Wind Industry 3. Analysis of Weather Factors and Uncertainty of a Random Forest Model in Wind Energy Forecasting 4. Enhancing Energy Demand Forecasting in Midtjylland, Denmark: A Machine Learning Approach Using LSTM and Weather-based Features 5. Concluding Remarks on Wind Energy and Weather Forecasting