Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead - Haytham Elmousalami; Aljawharah A. Alnaser; Felix Kin Peng Hui (Applied Sciences)
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.