Researchers from KAUST trained members of the Moving Windmills non-profit on green energy infrastructure. The training program included hands-on experience for installing solar photovoltaic systems for use in Malawi, such as solar water pumps and rooftop solar on school buildings. Moving Windmills will use this knowledge to coordinate energy projects across Malawi. Why it matters: This initiative highlights KAUST's commitment to supporting sustainable development in Africa by sharing technical expertise and resources.
Researchers propose a spatio-temporal model for high-resolution wind forecasting in Saudi Arabia using Echo State Networks and stochastic partial differential equations. The model reduces spatial information via energy distance, captures dynamics with a sparse recurrent neural network, and reconstructs data using a non-stationary stochastic partial differential equation approach. The model achieves more accurate forecasts of wind speed and energy, potentially saving up to one million dollars annually compared to existing models.
A novel wind speed forecasting (WSF) framework is proposed combining Wavelet Packet Decomposition (WPD), Seasonal Adjustment Method (SAM), and Bidirectional Long Short-term Memory (BiLSTM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The model was tested on five years of hourly wind speed observations acquired from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia, achieving high forecasting accuracy.
This paper introduces a novel fuzzy clustering method for circular time series based on a new dependence measure that considers circular arcs. The algorithm groups series generated from similar stochastic processes and demonstrates computational efficiency. The method is applied to time series of wind direction in Saudi Arabia, showcasing its practical potential.
Researchers introduce CESAR, a convolutional echo state autoencoder for high-resolution wind forecasting. The model extracts spatial features using a deep convolutional autoencoder and models their dynamics with an echo state network. Tested on high-resolution simulations in Riyadh, Saudi Arabia, CESAR improved wind speed and power forecasting by up to 17% compared to other methods. Why it matters: Accurate wind forecasting is critical for efficient wind farm planning and management in Saudi Arabia and the broader region.
KAUST researchers have developed solar panels with 4D-printed legs that readjust their position to track the sun's movement without consuming electrical energy. The design uses smart materials that contract when exposed to sunlight, tilting the panel towards the sun. A multidisciplinary team of interns collaborated on the project, integrating physics, electrical engineering, and mechanical engineering expertise. Why it matters: This low-cost, energy-efficient solar-tracking technology could significantly increase the energy output of solar cells, offering a viable renewable energy solution for the region and beyond.
MIT Professor Ahmed F. Ghoniem delivered a keynote at KAUST's Spring Enrichment Program discussing clean energy solutions for future cities. He emphasized a portfolio approach including electrochemical, solar thermochemical, and plasma technologies for renewable energy storage. Ghoniem highlighted the economic opportunities arising from clean energy technology deployment, R&D, and job creation. Why it matters: The focus on renewable energy and storage aligns with Saudi Arabia's Vision 2030 goals for sustainable urban development and diversification of the energy sector.
Carlos Duarte, a professor of Marine Science at KAUST, discusses climate change adaptation and mitigation. He was interviewed outside the KAUST Museum of Science and Technology. The interview is part of a Frontiers Research Topic on Climate Change Adaptation and Mitigation. Why it matters: This highlights KAUST's focus on addressing climate change through scientific research and its engagement with international platforms like Frontiers.