# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 8 Conclusions  
The Smart Wind Energy Deep Decarbonization (SWEDD) framework is an innovative approach designed to enhance the integration of wind energy into urban energy systems as a means to significantly reduce carbon emissions. This framework utilizes advanced AI algorithms to optimize wind energy forecasting, which is crucial for efficient wind farm management and energy supply adjustments in real time. By improving prediction accuracy across various time horizons, SWEDD supports the development of smart energy grids that are capable of handling the variability of wind power, thus facilitating the transition of urban environments from high carbon outputs to net-zero or carbon-negative statuses by calculating Carbon City Index (CCI) to track carbon-city transformation curve (CCTC). This incorporation of technology and renewable energy sources is pivotal in enabling cities to meet their decarbonization targets while maintaining energy security and economic viability.  
This paper presents a novel integrated wind speed-power forecasting system (WSPFS) that provides accurate predictions across multiple time scales, ranging from 10 min to 36 h ahead. By combining WSF and WPF into a cohesive model, this paper addresses a critical gap in wind energy prediction methodologies. The integrated approach ensures that the output of the wind speed forecasting model directly informs the wind power prediction model, enhancing the overall accuracy and reliability of forecasts. The results demonstrate that the extremely randomized trees (Extra Trees) and Bagging ensemble models are particularly effective, achieving MAPE and high adjusted R2 values across various time scales. This integration provides a robust tool for managing and optimizing wind energy generation, thereby contributing significantly to the field of renewable energy forecasting. As a result, a comprehensive methodology employed in this research, which includes a detailed comparison of 12 ML and DL algorithms, underscores the depth of analysis conducted. The study's evaluation of different models at multiple time scales from 10 min to 36 h offers valuable insights into performance metrics such as MAPE, MSE, and R2.  
The practical implications of this research are profound, particularly in the context of real-time energy management and regional energy collaboration. By improving the accuracy, the developed system supports better decision-making in grid management, energy trading, and policy development. The case study of the Gabal El Zeit station and the potential for regional cooperation with neighboring countries like Egypt, Jordan, and Saudi Arabia illustrates the broader impact of these findings. Additionally, the adaptability of the integrated prediction system to other renewable energy sources, such as solar and hydropower, suggests that the methodologies developed here could enhance forecasting accuracy and energy management on a global scale. Overall, this study advances the field of wind energy forecasting and provides a foundation for future innovations in renewable energy prediction and management.