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.
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.
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.
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.
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.
This paper introduces an AI-driven decision support system for green hydrogen investment in Oman, specifically for the Duqm R3 auction. The system uses publicly available meteorological data to predict maintenance pressure on hydrogen infrastructure, creating a Maintenance Pressure Index (MPI). This tool supports regulatory oversight and operational decision-making by enabling temporal benchmarking against performance claims.
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.
KAUST and K.A.CARE have partnered to study solar irradiation and atmospheric weather conditions in Saudi Arabia, leveraging K.A.CARE's Renewable Resources Atlas Project. The collaboration uses KAUST's Shaheen II supercomputer to simulate weather and atmospheric conditions from 2005-2018. The long-term goal is daily forecasting of weather and air quality across the Arabian Peninsula. Why it matters: This initiative will provide crucial data for renewable energy development and environmental monitoring in the region, supporting Saudi Arabia's sustainability goals.