# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 8 Conclusions
### Future Work  
Future work should focus on combining Numerical Weather Prediction (NWP) models with physics-informed deep learning (PIDL) to overcome some of the limitations associated with traditional machine learning and NWP integration [89]. Physics-informed deep learning directly incorporates physical laws and constraints into the learning process, allowing the model to leverage data-driven insights and domain-specific knowledge. By embedding the governing equations of fluid dynamics, thermodynamics, and atmospheric physics into the learning architecture, PIDL models can provide more accurate and generalizable predictions, even in regions or timeframes where observational data are sparse [90]. This approach helps mitigate the risk of overfitting historical data and allows the model to remain consistent with known physical principles, leading to more reliable and interpretable forecasts.  
In addition, integrating NWP with physics-informed deep learning could address challenges such as computational complexity and adaptability to changing weather conditions. PIDL models can be trained to prioritize computational efficiency by learning to approximate NWP outputs with lower computational costs while maintaining accuracy. In addition, they can dynamically adjust to new data or emerging patterns, making them more robust in real-time wind speed and power prediction scenarios. This hybrid approach, combining the rigor of physics-based models with the flexibility of deep learning, holds the potential to significantly enhance forecasting capabilities and pave the way for more efficient renewable energy management.  
After obtaining permission from the New and Renewable Energy Authority (NREA) in Egypt, the authors plan to test the model in real time on operational wind farms as part of future work to further validate the results. While the current study evaluates the models using historical data, real-time testing is essential for assessing the practical applicability and robustness of the proposed system under actual operating conditions. By deploying the model in real-world environments, the authors aim to determine its reliability, adaptability to dynamic weather conditions, and performance over extended periods. This real-time validation will also help identify potential challenges related to integration with existing energy systems and assess the computational efficiency of the model in live operational settings.  
Conceptualization, H.E. and A.A.A.; data curation, H.E.; formal analysis; H.E.; funding acquisition, A.A.A.; investigation, H.E., A.A.A. and F.K.P.H.; methodology, H.E.; project administration, H.E. and A.A.A.; resources, H.E. and A.A.A.; software, H.E.; supervision, H.E.; validation, H.E., A.A.A., and F.K.P.H; visualization, H.E., A.A.A., and F.K.P.H; roles/writing--original draft, H.E., A.A.A. and F.K.P.H.; writing--review and editing, H.E., A.A.A. and F.K.P.H. All authors have reviewed and approved the manuscript as written. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Researchers Supporting Project number (RSPD2024R590), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
Data Availability Statement: Data will be available upon request.  
The collected data of metrological data and the actual measurements of wind speed and wind power at Gabal El Zeit station (580 MW) have been provided by the New and Renewable Energy Authority (NREA) in Egypt and Engineer\(\backslash\)[PERSON].  
[PERSON] was employed by General Petroleum Company (GPC). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.