KAUST and the Saudi Electricity Company (SEC) collaborated to reduce non-technical losses in the Saudi power sector using machine learning. KAUST Visualization Core Lab (KVL) developed models using five years of SEC billing data from the Riyadh area to predict electricity usage and detect anomalous billing transactions. SEC estimates it could recover at least 73,000,000 SAR in lost revenue by correcting anomalies identified by KAUST models. Why it matters: This partnership demonstrates the potential of AI to address inefficiencies and fraud in critical infrastructure sectors in Saudi Arabia.
KAUST hosted Saudi Arabia's first National Hackathon of Energy, in partnership with the Saudi Electricity Company's Innovation Energy Incubator. Innovators collaborated to find technical solutions for the Kingdom's energy challenges. Three projects were honored: AI-HUB for cybersecurity, a smart circuit breaker, and 'Amn Al-Kafa’at,' a fire extinguisher system. Why it matters: This hackathon highlights the growing focus on using AI and technology to address energy challenges in Saudi Arabia, with KAUST playing a central role in fostering innovation.
A KAUST-led team in collaboration with Japan's National Institute of Informatics and Cray Inc. has implemented a new algorithm to harness the power of supercomputers. The algorithm integrates new singular value decomposition (SVD) codes into Cray LibSci scientific libraries, supporting machine learning and data de-noising applications. This was achieved through the Cray Center of Excellence (CCOE) at KAUST, established in 2015. Why it matters: The new algorithm helps to optimize the use of advanced supercomputing infrastructure in the region, specifically KAUST's Shaheen II, for computationally intensive AI applications.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
KAUST Ph.D. students Kai Lu and Yuqing Chen won Best Presentation awards at a Society of Exploration Geophysicists workshop in Beijing. Lu's research focuses on machine learning applications in seismic processing, while Chen uses machine learning for automated semblance spectrum picking. They both leverage KAUST's Shaheen II supercomputer for their work. Why it matters: This highlights the increasing role of AI and ML in the oil and gas industry, and KAUST's contribution to advancing these technologies.