Dr. Elnaz Yaghoubi, karabuk university, Turkey.
Elnaz Yaghoubi is a dedicated Ph.D. candidate in Electronic and Electrical Engineering at Karabuk University, Turkey, boasting a stellar GPA of 4.0. Her research specializes in power system analysis, microgrids, and renewable energy. Elnaz holds an M.Sc. in Electrical Engineering from Islamic Azad University, also achieving a perfect GPA. She has worked as an engineering expert at Iran’s Telecommunication Company and is an active member of the PEDAR research group, contributing to innovative projects in smart grid technology. With a passion for advancing energy solutions, Elnaz is a rising star in her field. 
Professional Profile:
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Education and Experience:
- Ph.D. in Electronic and Electrical Engineering
Karabuk University, Turkey (2021-Present)
Thesis: Techno-economical reliable energy management of smart microgrids
GPA: 4.0 
- M.Sc. in Electrical Engineering
Islamic Azad University, Qaemshahr, Iran (2016-2018)
Thesis: New topology based on clustering for network on chip
GPA: 4.0
- B.Sc. in Electrical Engineering
Aryan Institute of Science and Technology, Iran (2012-2014)
GPA: 4.0
- Associate’s Degree in Electrical Engineering
University College of Rouzbahan, Iran (2010-2012)
GPA: 4.0
- Principle Researcher
PEDAR Group (2023-Present) 
- Expert in Traffic Monitoring and Data Support
Telecommunication Company, Iran (2017-2021) 
- Data Network Design
Telecommunication Company, Iran (2015-2017) 
Suitability for Best Researcher Award:
Dr. Elnaz Yaghoubi is an exemplary candidate for the Best Researcher Award in the field of Electronic and Electrical Engineering due to her academic excellence, impactful research contributions, and professional experience.
Professional Development:
Elnaz Yaghoubi is continually enhancing her skills and expertise through various professional development avenues. She possesses strong programming skills in MATLAB and C++ and is currently expanding her knowledge in Python and Linux Essentials. With experience in machine learning and deep learning techniques, she actively engages in research and development within the PEDAR research group. Her proficiency in software tools like AutoCAD and Proteus, alongside certifications in network and security fundamentals, underlines her commitment to staying at the forefront of technological advancements in power systems. 
Research Focus:
Elnaz Yaghoubi’s research primarily revolves around power system analysis, focusing on optimizing power management in microgrids and smart grids. She explores renewable energy solutions and the integration of distributed generation methods to enhance energy efficiency. Additionally, Elnaz delves into model predictive control (MPC) for advanced power control strategies, emphasizing cyber security in energy systems. Her work with artificial neural networks and machine learning further supports innovative solutions in the field. Elnaz’s commitment to addressing contemporary energy challenges makes her a pivotal figure in advancing smart energy technologies. 
Awards and Honors:
- Perfect GPA Award (Karabuk University)
- Outstanding Research Contribution Award (Islamic Azad University)
- Best Paper Award (Conference on Renewable Energy Solutions)
- Excellence in Engineering Award (Telecommunication Company, Iran)
- Leadership in Research Award (PEDAR Group)
Publication top Notes:
- State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Cited by: 85 | Year: 2023
- The role of mechanical energy storage systems based on artificial intelligence techniques in future sustainable energy systems
Cited by: 15 | Year: 2023
- Triple-channel glasses-shape nanoplasmonic demultiplexer based on multi nanodisk resonators in MIM waveguide
Cited by: 12 | Year: 2021
- Reducing the vulnerability in microgrid power systems
Cited by: 11 | Year: 2023
- Electric vehicles in China, Europe, and the United States: Current trend and market comparison
Cited by: 10 | Year: 2024
- Tunable band-pass plasmonic filter and wavelength triple-channel demultiplexer based on square nanodisk resonator in MIM waveguide
Cited by: 10 | Year: 2022
- A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior
Cited by: 9 | Year: 2024