Mrs. Inajara Rutyna | Online Monitoring | Best Researcher Award
Mrs. Inajara Rutyna | Online Monitoring | Warsaw University of Technology | Poland
Mrs. Inajara Rutyna is a distinguished researcher in the field of Artificial Intelligence and Renewable Energy Systems, currently pursuing her Ph.D. in Automation, Electronics, and Electrical Engineering at the Warsaw University of Technology, Poland. Her academic foundation is built on a Master’s degree in Numerical Methods in Engineering and a Bachelor’s degree in Industrial Mathematics from the Universidade Federal do Paraná, Brazil. Throughout her academic and professional journey, Mrs. Inajara Rutyna has consistently demonstrated exceptional proficiency in mathematical modeling, computational intelligence, and optimization methods. Her professional experience encompasses diverse roles, including AI Development Specialist at IDEAS NCBR Sp. z o.o., where she developed intelligent algorithms and Python-based models for renewable energy forecasting, and Mathematical Modeller and Data Scientist at the National Centre for Nuclear Research, Poland, contributing to mathematical frameworks for sustainable power systems. Additionally, her earlier engagements as a Game Economy Designer at Rage Quit Games and as a Project and Process Analyst at Segula do Brasil Engenharia e Tecnologia reflect her versatility in applying data-driven modeling to industrial, gaming, and energy contexts. Mrs. Rutyna’s research interests lie primarily in Artificial Intelligence applications for renewable energy forecasting, computational fluid dynamics, optimization algorithms, and machine learning-based energy modeling. Her technical skills include advanced programming in Python, MATLAB, and Fortran, as well as expertise in numerical analysis, data science, and algorithmic development. She has authored and co-authored multiple IEEE and Scopus-indexed publications focusing on energy efficiency prediction, evaluation metrics for wind power, and AI-based forecasting. She is an active member of professional bodies such as the IEEE, contributing to international research collaborations and scientific discussions on sustainable technology innovation.
Professional Profiles: ORCID
Featured Publications
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Rutyna, I. (n.d.). Gated lag and feature selection for day-ahead wind power forecasting using on-site SCADA data. IEEE. (Citations: 42)
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Rutyna, I. (n.d.). Efficiency analysis of k-nearest neighbors machine learning method for 10-minutes ahead forecasts of electric energy production at an onshore wind farm. Elsevier. (Citations: 38)
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Rutyna, I. (n.d.). Evaluation metrics for wind power forecasts: A comprehensive review and statistical analysis of errors. IEEE Access. (Citations: 57)
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Rutyna, I. (n.d.). Polynomial interpolation with repeated Richardson extrapolation to reduce discretization error in CFD. Springer. (Citations: 31)
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Rutyna, I. (n.d.). Stochastic hybrid optimization methods for renewable energy forecasting and grid stability. IEEE Transactions on Sustainable Energy. (Citations: 29)