Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
Source abstract
Abstract
Accurate wind speed and power forecasting are key to optimizing renewable wind station management, which is essential for smart and zero-energy cities. This paper presents a novel integrated wind speed–power forecasting system (WSPFS) that operates across various time horizons, demonstrated through a case study in a high-wind area within the Middle East. The WSPFS leverages 12 AI algorithms both individual and ensemble models to forecast wind speed (WSF) and wind power (WPF) at intervals of 10 min to 36 h. A multi-horizon prediction approach is proposed, using WSF model outputs as inputs for WPF modeling. Predictive accuracy was evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). Additionally, WSPFS advances the smart wind energy deep decarbonization (SWEDD) framework by calculating the carbon city index (CCI) to define the carbon-city transformation curve (CCTC). Findings from this study have broad implications, from enabling zero-energy urban projects and mega-developments like NEOM and the Suez Canal to advancing global energy trading and supply management.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Solar Radiation and Photovoltaics · Air Quality Monitoring and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC 3010, Australia
General Petroleum Company (GPC), Cairo 743, Egypt
Department of Architecture and Building Science, Collage of Architecture and Planning, King Saud University, Riyadh 11574, Saudi Arabia
Funding
Researchers Supporting Project · King Saud University, Riyadh, Saudi Arabia
References (90)
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