Abdul Azzam

Scientific Publications:

• Google Scholar

Student Theses:

• Tim Dubies: "Development and Analysis of a Model-Predictive Control for an optimized Operation of Heating Grids in Energy Quarters," Master's Thesis, 2024
  • In this master's thesis, a simulation model for district heating networks was developed and analyzed. The goal was to compare the efficiency and effectiveness of different control systems for district heating networks. The district heating network model combines several sub-models and includes a combined heat and power plant, solar thermal energy, a heat pump, and a thermal storage unit. The model was created using the Python library PyDHN. A model predictive controller (MPC) was implemented and compared with a simple rule-based control (RBC). Each component of the system was approximated by a linear model, and the fuel and electricity costs of the generators were included in the objective function. The results are based on data from the district heating network at the University of Stuttgart campus. Various combinations of heat generators were examined to represent different stages of modernization and decarbonization. The studies show that the advantages of model predictive control become particularly evident when multiple generators and storage units can be operated flexibly. The integration of intraday electricity prices into the cost function led to an intensive use of this flexibility and minimized the total costs of heat generation. However, some inaccuracies of the predictive controller were also identified, leading to suboptimal operation and increased costs. This work provides valuable insights into the control and optimization of district heating networks and presents important considerations for the future modernization and decarbonization of such networks.
• Lakshay Panjwani: "Structuring energy data using a database to evaluate ML models for load forecasts," Master's Thesis, 2024
  • This master's thesis focuses on the development of machine learning models for the precise forecasting of energy consumption in buildings, contributing to more efficient energy management. Various models were analyzed, including linear regression, ridge regression, SARIMA, and LightGBM. External factors such as weather data, holidays, and building-specific characteristics were considered to improve forecasting accuracy. Additionally, a software architecture was developed for managing and processing energy data using a database management system (DBMS), enabling efficient handling of various data types, such as time series data, and ensuring that data can be easily shared among all stakeholders. The results of this work show that the SARIMA model achieved the highest forecasting accuracy, outperforming other models. The developed DBMS architecture proved to be an efficient way to organize datasets, process energy data, and support reliable forecasts, contributing to improved decision-making in energy management. In conclusion, the combination of advanced machine learning methods with powerful data management systems is crucial for developing accurate and scalable energy forecasting solutions. The modular software architecture is flexible and adaptable, allowing for future enhancements such as the integration of real-time data and advanced analytical tools.

Experience:

• Current Position:
  • PhD Student, Institute for Energy Economics and Rational Energy Use, University of Stuttgart (since 2023) Project: Discursive Transformation of Energy Systems (DiTEnS)
  • Lecturer, DHBW Stuttgart (September 2023 - present) Lectures: Fundamentals of Automation, Control Engineering
• Previous Positions:
  • Research Thesis, Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) (August 2021 - October 2022): Development and Analysis of a Distributed Predictive Control Algorithm for Smart Grids with Decentralized Virtual Power Plants
  • Research Assistant, Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) (July 2019 - September 2021)
  • Technical Assistant, Nagel Machines and Tool Factory GmbH (October 2018 - May 2019)
  • Cooperative Studies Mechanical Engineering (DHBW), bielomatik GmbH (October 2015 - September 2018)

Education:

• University of Stuttgart: Master of Science - MS, Engineering Cybernetics (April 2019 - March 2023) Fields: Autonomous Systems & Control Theory, Automation in Energy Engineering, Artificial Intelligence, Computer Science
• Universitat de Barcelona: Master of Science - MS (February 2022 - July 2022) Fields: Neurobiology and Neuroscience, Parallel Programming (CUDA and OpenMP)
• Hochschule Esslingen - University of Applied Science: Master of Engineering, Systems Engineering/Mechatronics (October 2018 - April 2019) Fields: Control Engineering & Numerical Mathematics
• Baden-Wuerttemberg Cooperative State University (DHBW): Bachelor of Engineering - BE, Mechanical Engineering (September 2015 - September 2018) Field: Design & Development