Khaled Alrashed, president and CEO of Saudi Electricity Company for Projects Development, discussed the challenges of future smart cities at a KAUST event. He emphasized the importance of smart grids, AI, and large-scale optimization for improving urban living. The Saudi Electricity Company is partnering with KAUST, including using the Shaheen supercomputer, to develop these technologies and predict grid load. Why it matters: This collaboration highlights Saudi Arabia's ambition to become a leader in smart city technology and renewable energy, leveraging local expertise and resources.
QualSens, a KAUST startup, is developing a smart sensor for monitoring and enhancing process control in water desalination systems. The sensor uses fluorescent enzymatic sensing to detect bacterial activity and fouling at an early stage. The system alerts operators to start cleaning the system based on the sensor feedbacks, helping to decrease energy demand for drinking water production. Why it matters: This technology could significantly improve the efficiency and reduce the costs of desalination, a critical process for water security in the Middle East.
A team of KAUST researchers led by Abdulwahab Felemban won first place in the Digital Research track at the inaugural Digital Innovation Awards from MCIT for their AI-driven water tap, Smart-Tap. Smart-Tap uses AI to personalize water flow and pressure, reducing waste by up to 43% compared to infrared taps. The project was inspired by the water waste observed during the Wudu ritual. Why it matters: This award highlights the potential of AI-driven solutions developed in Saudi Arabia to address critical sustainability challenges like water conservation, aligning with the Kingdom's National Water Strategy 2030.
The KAUST Smart Home has received LEED Platinum certification, ranking second globally with a score of 94. The project retrofitted an existing home with features like 120 solar panels and hydropanels producing drinking water from atmospheric humidity. It also includes a leak detection system and smart fill windows controllable via touch panel. Why it matters: This demonstrates how existing infrastructures in the region can be improved to meet sustainability goals using smart technology.
MBZUAI researchers are using federated learning to optimize energy production and use in microgrids, balancing individual and grid-level needs with a focus on sustainability. They presented a multi-agent framework called MAHTM at the ICLR 2023 workshop, aiming to minimize the carbon footprint of electrical grids. The system uses three layers of decision-making agents to minimize cost, decrease carbon impact, and balance production. Why it matters: This research offers a novel approach to integrating renewable energy sources into existing grids, potentially accelerating the transition to more sustainable energy systems in the region and globally.
This paper presents a reinforcement learning framework for optimizing energy pricing in peer-to-peer (P2P) energy systems. The framework aims to maximize the profit of all components in a microgrid, including consumers, prosumers, the service provider, and a community battery. Experimental results on the Pymgrid dataset demonstrate the approach's effectiveness in price optimization, considering the interests of different components and the impact of community battery capacity.