M.Sc. Syed Irtaza Haider
PhD Researcher

Syed Irtaza Haider holds a Master of Science in Electrical Engineering from King Saud University (KSU), Saudi Arabia, where he was awarded the prestigious Prince Sultan Scholarship. He completed his bachelor’s degree in Electrical Engineering with a specialization in Electronics from the National University of Sciences and Technology (NUST), Pakistan, earning merit scholarships for three semesters. He has been actively involved in research projects focusing on evolutionary computing, machine/deep learning, and the application of AI in smart grids and data science.

He is currently a Ph.D. student at TU Dresden’s Deutsche Telekom Chair of Communication Networks, working on the DymoBat project, which aims to develop marketable solutions for future energy grid management using distributed energy resources and 5G technologies.

Contact

Email: syed_irtaza.haider@tu-dresden.de
Room: BAR S/16

Phone: +49 176 75385658

Research Interests

  • Vehicle-to-Grid (V2G) Technology
  • Holistic Integration of Energy Systems
  • Optimization in Smart Grids

Publications

2024

K. Aurangzeb, S. I. Haider and M. Alhussein, “Individual Household Load Forecasting using Bi-directional LSTM Network with Time-based Embedding,” Energy Reports, vol. 11, pp. 3963-3975, Apr 2024.

2023
M. Ikram, M. Aslam, K. Aurangzeb, S. Ahmed, S. N. K. Marwat, S. I. Haider and M. Alhussein, “Integrating renewable energy sources for optimal demand-side management using decentralized multi-agent control,” Sustainable Energy, Grids and Networks, vol. 36, Dec 2023.

A. Alzahrani, G. Hafeez, G. Rukh, S. Murawwat, F. Iftikhar, S. Ali, S. I. Haider, M.I. Khan and A.M Abed, “Demand response for optimal power usage scheduling considering time and power flexibility of load in smart grid,” IEEE Access, vol. 11, pp. 33640 – 33651, April 2023.

2022
F. R. Albogamy, S. A. Khan, G. Hafeez, S. Murawwat, S. Khan, S. I. Haider, A. Basit and K-D Thoben, “Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid,” Sustainability, vol. 14, no. 3, pp. 1 – 28, Feb 2022.

2021
S. Aslam, N. Ayub, U. Farooq, M. J. Alvi, F. R. Albogamy, Gul Rukh, S. I. Haider, A. T. Azar and R. Bukhsh, “Towards Electric Price and Load Forecasting Using CNN-Based Ensemble in Smart Grid,” Sustainability, vol. 13, no. 22, pp. 1 – 28, Nov 2021.

K. Aurangzeb, M. Alhussein, K. Javaid, and S. I. Haider, “A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering,” IEEE Access, vol. 9, pp. 14992 – 15003, Jan 2021.

2020
M. Alhussein, K. Aurangzeb and S. I. Haider, “Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting,” IEEE Access, vol. 8, pp. 180544 – 180557, Oct 2020.

2019
M. Alhussein, S. I. Haider, and K. Aurangzeb, “Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance,” Energies, vol. 12, no. 8, Apr 2019.

K. Aurangzeb, S. Aslam, S. I. Haider, S. M. Mohsin, S. Islam, H. A. Khattak, S. Shah, “Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system,” Transactions on Emerging Telecommunications Technologies, e3837, pg. 1 – 14, Dec 2019.