Prof. Dr. Fatih Onur Hocaoğlu
Afyon Kocatepe University, Turkey
Research Areas: Electrical Energy and Power Systems, Renewable Energy Systems, Signal Processing, High Voltage
Biography: Fatih Onur Hocaoğlu was born in Afyonkarahisar, Turkey, in 1979. He received the B.Sc. and Ph.D. degrees in electrical engineering from Pamukkale University, Denizli in 2002 and Anadolu (Eskisehir Technical) University, Eskişehir in 2008, respectively. He is currently a full Professor at Afyon Kocatepe University. He is currently the director of Electrical Engineering Department, Solar and Wind Energy Research and Application Center and ÇAY Vocational High School at Afyon Kocatepe University. His current research interests are in electric power systems and renewable energy systems, emphasizing signal processing applications on renewable data, innovative approach development on energy forecasting, optimization of renewable energy systems, simulation, analysis and control of smart grids, power systems of electrical cars and low carbon emission handling system design. He has published more than 200 technical papers and conducted several research activities. He holds 2 patents and 1 Design at national patent institution of Turkey. He is listed among 2% of “the World's Most Influential Scientists” as a result of the survey carried out by Stanford University in 22 scientific disciplines and 176 sub disciplines in 2022.
Speech Title: Intelligent and innovative systems for renewable data prediction and a close look to a novel tool
Abstract: Renewable energy systems get more attraction with the need for clean and sustainable energy. Usage of such sources has increased significantly in recent years due to the wars and the increasing need of electricity for new technologies such as electrical cars. However, power fluctuations due to stochastically behavior of the sources are among the main challenges in using such systems. Therefore, it is of vital importance to develop accurate models to predict possible energy generation from such sources in a period of time at any region. Traditional approaches handle the data as one dimensional time series data. However, using different models it is possible to process the data in a different point of view. However each data has its own characteristic feature and in case the feature has been detected, it is possible to improve the accuracy of prediction model. In this speech, it is aimed to give these insights to the listeners by presenting a different perspective of view to wind and solar radiation data. Novel published models on this perspective will be introduced. Hidden internal features of the data and the way of how it is possible to make this internal feature available, will be discussed. Finally, an open access tool developed by the team of speaker to forecast any renewable data will be introduced at the end of the speech.