Doctor of Philosophy (PhD)
Electrical and Computer Engineering
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
Fourth Advisor's Name
Fourth Advisor's Committee Title
Demand Side Management, Artificial Intelligent, Energy Management, Non Intrusive Load Monitoring, Short Term Load Forecasting, Energy Disaggregation, Deep Learning, Microgrid, Nanogrid, Smart Home
Date of Defense
The rapid development of various power electronics applications facilitates the integration of many smart grid applications in recent years. However, integration of intermittent renewable energy sources, highly stochastic electric vehicles (EVs) activities on the grid and time-varying smart loads have increased the level of grid vulnerability to unusual and high complexity and quality-related problems. Among these problems is to accurately estimate the real contribution and consumption of household loads, in the era of smart appliances and interoperability operation, and its relative impact to the grid’s operation. Specifically, household loads represent a significant percentage of electrical energy consumption and, therefore, could offer great prosperity to the rise of the demand-side management (DSM) programs, which subsequently improve the stability of the grid’s operation. As a result, our main focus in this dissertation is to develop DSM strategies based on Artificial Intelligence (AI) techniques to properly model and estimate the amount of support smart homes could offer to the smart grids and microgrid’s operation.
Throughout the way to achieve our goals, we develop an energy management framework for smart homes that operate in efficient and reliable microgrids with multiple energy sources and energy storage applications to meet the demands at a stable voltage and frequency limits. Furthermore, we develop a precise short-term load forecasting (STLF), which is a critical tool needed to manage a DSM program for residential loads that have very high uncertainty and volatility in load consumption. We also develop an energy exchange portal with communication sources, demands, and connectivity information between each consumer and the local power utility at the distribution level. Finally, creative AI methodologies have been developed throughout the way to facilitate the integration, control, and management of the DSM programs taking into account the consumers’ own privacy and security. The security of the DSM is provided by preserving the indoor privacy of the smart homes by sharing limited and encoded data among household appliances controllers.
Previously Published In
- Ahmed. F. Ebrahim, Osama A. Mohammed,” Pre-Processing of Energy Demand Disaggregation Based Data Mining Techniques for Household Load Demand Forecasting. Inventions 2018, 3, 45.
- Ahmed. F. Ebrahim, Ahmed A. Saad, Osama A. Mohammed,” Smart Integration of a DC Microgrid: Enhancing the Power Quality Management of the Neighborhood Low-Voltage Distribution Network. Inventions 2019, 4(2), 25.
- Tawfiq M. Aljohani, Ahmed. F. Ebrahim, Osama A. Mohammed,” Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization. Energies 2019, 12(12), 2333.
- Fatima Zahra Harmouch, Ahmed. F. Ebrahim, Mohammad M. Esfahani, Nissrine Krami, Nabil Hmina, Osama A. Mohammed," An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm”. Energies 2019, 12 (5), 3004.
- Ahmed. F. Ebrahim and O. A. Mohammed, "Energy Disaggregation Based Deep Learning Techniques: A pre-processing Stage to Enhance the Household Load Forecasting," 2018 IEEE Industry Applications Society Annual Meeting (IAS), Portland, OR, USA, 2018, pp. 1-8.
- Ahmed. F. Ebrahim and O. Mohammed, "Household Load Forecasting Based on a Pre-Processing Non-Intrusive Load Monitoring Techniques," 2018 IEEE Green Technologies Conference (GreenTech), Austin, TX, 2018, pp. 107-114. DOI: 10.1109/GreenTech.2018.00028
- Ahmed. F. Ebrahim, A. A. S. Mohamed, A. A. Saad and O. A. Mohammed, "Vector Decoupling Control Design Based on Genetic Algorithm for a Residential Microgrid System for Future City Houses at Islanding Operation," SoutheastCon 2018, St. Petersburg, FL,2018,pp.1-5.doi: 10.1109/SECON.2018.8479013.
- Ahmed. F. Ebrahim, Tarek A. Youssef, Osama A. Mohammed, " Power Quality Improvements for Integration of Hybrid AC/DC Nanogrids to Power Systems” IEEE Greentech 2017, Denver, Colorado, held between March 29 – 31, 2017.
- Ahmed. F. Ebrahim, S. M. W. Ahmed, S. E. Elmasry and O. A. Mohammed, "Implementation of a PV emulator using programmable DC power supply," SoutheastCon 2015, Fort Lauderdale, FL, 2015, pp. 1-7. DOI: 10.1109/SECON.2015.7133048.
- A. Elsayed, Ahmed. F. Ebrahim, H. Mohammed, and O. A. Mohammed, "Design and implementation of AC/DC active power load emulator," SoutheastCon 2015, Fort Lauderdale, FL, 2015, pp. 1-5. DOI: 10.1109/SECON.2015.713291.
- Ahmed. F. Ebrahim, T. Youssef, S. M. W. Ahmed, S. E. Elmasry and O. A. Mohammed, "Fault detection and compensation for a PV system grid-tie inverter," 2014 North American Power Symposium (NAPS), Pullman, WA, 2014, pp. 1-6.doi: 10.1109/NAPS.2014.6965470.
- Ahmed. F. Ebrahim, N. Elsayad and O. A. Mohammed, "Medium Voltage DC Testbed: A Hardware-Based Tool to Integrate DC Microgrids/Nanogrids to the Utility Infrastructure," 2018 IEEE Industry Applications Society Annual Meeting (IAS), Portland, OR, USA, 2018, pp. 1-7.
EBRAHIM, AHMED F., "Artificial Intelligent Based Energy and Demand Side Management for Microgrids and Smart Homes Considering Customer Privacy" (2020). FIU Electronic Theses and Dissertations. 4424.
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