Adaptive decision-making algorithm
A dynamic load-management approach that updates switching decisions using real-time battery status, forecast solar generation, expected demand, and load priorities—rather than relying only on fixed rules.
Education & research
Cyber-Physical Systems
Connecting software, sensing, communications, and intelligent control to practical engineering challenges.
Graduate studies
Master's in Cyber-Physical Systems
I am pursuing a Master's degree at Al-Aqsa University. Coursework is completed, and the thesis remains in progress.
Current thesis title
CPS-Based Smart Adaptive Energy Management Framework for Improving Solar Energy Utilization in Gaza Strip
The research proposes a Cyber-Physical Systems (CPS) framework that combines Internet of Things (IoT) sensing and AI-based forecasting to help households use available solar energy more effectively under changing, non-ideal, and resource-constrained conditions.
Research overview
Research idea
The framework is designed for residential solar photovoltaic (PV) systems affected by variable sunlight, changing household demand, aging batteries, and potentially damaged components. It will monitor solar generation, battery condition, and household loads in real time; estimate and forecast energy availability and demand; prioritize critical loads; and adaptively switch non-critical or deferrable loads. When suitable surplus solar energy would otherwise go unused, the framework may redirect it to a useful deferrable load, such as water heating.
The proposed system will be evaluated through MATLAB/Simulink simulations and a small ESP32-based hardware prototype using power sensors and relay modules. The proposal also draws on a 13-day field observation of a residential PV system in Gaza to ground the research in practical operating challenges.
Expected scientific contributions
What the research aims to contribute
Integrated CPS framework
A unified design combining monitoring, forecasting, battery-condition assessment, load prioritization, and smart switching within a practical residential energy-management workflow.
Lightweight, resource-aware AI
An evaluation of forecasting and decision approaches for computational cost, response time, memory needs, and suitability for an ESP32-based or other constrained edge deployment.
Field-grounded evaluation
A simulation-and-prototype evaluation approach informed by real operating observations from a residential PV system in Gaza, with relevance to similarly resource-constrained settings.
Expected outcomes
What will be evaluated
The expected outcome is a practical, low-cost framework that can improve the use of available solar energy, reduce avoidable energy waste, and help maintain critical household loads as generation, battery performance, and demand change. These are research objectives, not yet validated results.
The framework's performance will be assessed using these indicators in simulation and prototype testing. No numerical improvement is claimed before evaluation is completed.
Research themes
Areas of interest
IoT & Monitoring
Collecting data from physical systems and using it to understand operating conditions.
Edge AI
Exploring lightweight machine-learning approaches that can operate close to the data source.
Energy Management
Prioritizing household loads and making better use of available solar energy.
Resilient CPS Design
Practical cyber-physical solutions designed around limited resources and unreliable connectivity.
This section describes my academic focus and proposed research direction; it does not claim that results have been published or validated.
Academic collaboration
Get in touch
For research discussions and collaboration enquiries.