Electrical and Electronics Engineering at karabuk university, Turkey
https://new-science-inventions.sciencefather.com/elnaz-yaghoubi-energy-best-researcher-award-24494/Elnaz Yaghoubi is a seasoned researcher with a notable career in both academia and industry. She has been leading research initiatives at PEDAR Group since 2023, focusing on innovative solutions for power systems and microgrids. Her work emphasizes enhancing the security, efficiency, and resilience of these systems through advanced methodologies. Elnazโs professional background also includes significant experience in traffic monitoring and data support, as well as data network design, which complements her research efforts.
Professional Profile:
Education๐ย
Elnaz Yaghoubiโs educational background underpins her expertise in power systems and microgrid technologies. She has a comprehensive foundation in her field, supported by advanced degrees and specialized knowledge:
- Current Pursuit: Elnaz is actively pursuing advanced research in her field through her current position as Principal Researcher at the Power Electrical Developing Advanced Research (PEDAR) Group.
- Previous Education: Her previous education includes specialized training and experience in data networks and telecommunications, which she applied in her professional roles.
Professional Experience ๐ผ
Elnaz Yaghoubi has a robust background in both academia and industry, which is evident from her professional experience and research publications. She currently serves as the Principal Researcher at the Power Electrical Developing Advanced Research (PEDAR) Group, a role she has held since 2023. This position signifies a leadership role in a research-focused environment, suggesting her capacity to guide complex projects and contribute to cutting-edge advancements in power systems and microgrid technologies.
Research Skill๐
Elnaz Yaghoubi possesses a diverse set of research skills that are critical to her role and contributions:
- Advanced Analytical Techniques: She employs sophisticated methods such as deep learning and model predictive control to address challenges in smart power microgrids.
- Cybersecurity Expertise: Her research focuses on enhancing security in power systems, highlighting her skill in tackling contemporary issues related to cyber-attacks and data integrity.
- Collaborative Research: Elnazโs ability to collaborate with other experts, as seen in her co-authored publications, demonstrates her proficiency in working within multidisciplinary teams and contributing to high-impact research.
Award and Honor
Elnaz Yaghoubiโs achievements and contributions to her field have been recognized through various accolades and awards. Some highlights include:
- Global Recognition: Her innovative research and leadership in power systems have earned her recognition in international research communities.
- Emerging Achievements: Her papers and research have been well-received, with several notable publications under review in leading journals, reflecting her growing influence and reputation in her field.
Publications top noted๐
1. State-of-the-Art Review on Energy and Load Forecasting in Microgrids Using Artificial Neural Networks, Machine Learning, and Deep Learning Techniques
- Authors: R. Wazirali, E. Yaghoubi, M.S.S. Abujazar, R. Ahmad, A.H. Vakili
- Journal: Electric Power Systems Research
- Volume/Page: 225, 109792
- Year: 2023
- Summary: This review explores the latest advancements in forecasting energy and load requirements in microgrids through artificial neural networks, machine learning, and deep learning techniques. The paper discusses various methodologies and their effectiveness in improving the accuracy of forecasts in microgrid systems.
2. The Role of Mechanical Energy Storage Systems Based on Artificial Intelligence Techniques in Future Sustainable Energy Systems
- Authors: M. Khaleel, E. Yaghoubi, E. Yaghoubi, M.Z. Jahromi
- Journal: International Journal of Electrical Engineering and Sustainable
- Volume/Page: 01-31
- Year: 2023
- Summary: This paper investigates the impact of mechanical energy storage systems on sustainable energy systems, focusing on the integration of artificial intelligence techniques to enhance system efficiency and sustainability.
3. Triple-Channel Glasses-Shape Nanoplasmonic Demultiplexer Based on Multi-Nanodisk Resonators in MIM Waveguide
- Authors: A.A. Faghani, E. Yaghoubi, E. Yaghoubi
- Journal: Optik
- Volume/Page: 237, 166697
- Year: 2021
- Summary: This research introduces a nanoplasmonic demultiplexer with a triple-channel glasses-shape configuration using multi-nanodisk resonators in a metal-insulator-metal (MIM) waveguide, aimed at advancing optical communication technologies.
4. Electric Vehicles in China, Europe, and the United States: Current Trend and Market Comparison
- Authors: M. Khaleel, Y. Nassar, H.J. El-Khozondar, M. Elmnifi, Z. Rajab, E. Yaghoubi, et al.
- Journal: International Journal of Electrical Engineering and Sustainable
- Volume/Page: 1-20
- Year: 2024
- Summary: This article presents a comparative analysis of electric vehicle trends and market dynamics across China, Europe, and the United States, highlighting key factors influencing the adoption and development of electric vehicles.
5. Tunable Band-Pass Plasmonic Filter and Wavelength Triple-Channel Demultiplexer Based on Square Nanodisk Resonator in MIM Waveguide
- Authors: A.A. Faghani, Z. Rafiee, H. Amanzadeh, E. Yaghoubi, E. Yaghoubi
- Journal: Optik
- Volume/Page: 257, 168824
- Year: 2022
- Summary: This study proposes a tunable band-pass plasmonic filter and a wavelength triple-channel demultiplexer utilizing a square nanodisk resonator in an MIM waveguide, contributing to the field of plasmonic devices and optical filtering.
6. Reducing the Vulnerability in Microgrid Power Systems
- Authors: Z. Yusupov, E. Yaghoubi, V. Soyibjonov
- Journal: Science and Innovation
- Volume/Page: 2 (A5), 166-175
- Year: 2023
- Summary: This paper addresses strategies for minimizing vulnerabilities in microgrid power systems, focusing on enhancing resilience and security against potential disruptions.
7. A Systematic Review and Meta-Analysis of Artificial Neural Network, Machine Learning, Deep Learning, and Ensemble Learning Approaches in the Field of Geotechnical Engineering
- Authors: E. Yaghoubi, E. Yaghoubi, A. Khamees, A.H. Vakili
- Journal: Neural Computing and Applications
- Volume/Page: 1-45
- Year: 2024
- Summary: This review provides a comprehensive analysis of various learning approaches applied to geotechnical engineering, including artificial neural networks, machine learning, deep learning, and ensemble learning, assessing their effectiveness and trends.
8. Controlling and Tracking the Maximum Active Power Point in a Photovoltaic System Connected to the Grid Using the Fuzzy Neural Controller
- Authors: Z. Yusupov, E. Yaghoubi, E. Yaghoubi
- Conference: 2023 14th International Conference on Electrical and Electronics Engineering
- Year: 2023
- Summary: This conference paper discusses the use of a fuzzy neural controller to manage and track the maximum active power point in photovoltaic systems integrated with the grid, aiming to improve energy efficiency and system performance.
9. Modeling and Control of Decentralized Microgrid Based on Renewable Energy and Electric Vehicle Charging Station
- Authors: Z. Yusupov, N. Almagrahi, E. Yaghoubi, E. Yaghoubi, A. Habbal, D. Kodirov
- Conference: World Conference on Intelligent Systems for Industrial Automation
- Volume/Page: 96-102
- Year: 2022
- Summary: This paper presents a model and control strategy for decentralized microgrids incorporating renewable energy sources and electric vehicle charging stations, addressing challenges and solutions in integrated microgrid management.
10. A Systematic Review and Meta-Analysis of Machine Learning, Deep Learning, and Ensemble Learning Approaches in Predicting EV Charging Behavior
- Authors: E. Yaghoubi, E. Yaghoubi, A. Khamees, D. Razmi, T. Lu
- Journal: Engineering Applications of Artificial Intelligence
- Volume/Page: 135, 108789
- Year: 2024
- Summary: This review explores predictive models for electric vehicle (EV) charging behavior using machine learning, deep learning, and ensemble learning approaches, evaluating their accuracy and applicability in forecasting charging patterns.
