# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 8 Conclusions
### Research Limitations  
To ensure the credibility and comprehensiveness of the study, it is essential to address ethical concerns such as algorithmic bias and data privacy. ML models can inadvertently reflect biases present in the training data, leading to unequal outcomes in wind power predictions across different geographic regions or weather conditions. For instance, if the dataset predominantly represents certain locations or weather patterns, the algorithm may underperform in less-represented areas, resulting in less accurate predictions. Such biases could affect the reliability of wind energy systems, particularly in regions where precise forecasting is critical for grid management and energy supply. To mitigate this risk, diverse and representative data should be incorporated, and model performance should be monitored across various contexts to ensure fairness and accuracy. Additionally, data privacy concerns arise as these models require vast amounts of meteorological and operational data, which could include sensitive information about energy usage patterns.  
Safeguarding this data through encryption, anonymization, and stringent access control protocols is vital to maintain trust and prevent misuse.  
Moreover, considering the practical applications of ML models in renewable energy management, their potential societal and environmental impact must be critically examined. The integration of such predictive systems into energy grids could transform the way wind power is harnessed and utilized, leading to improved efficiency and sustainability. However, the deployment of these models at scale necessitates transparency in their decision-making processes, particularly in critical sectors such as energy supply, where errors or inefficiencies can have wide-reaching consequences. Regulators and stakeholders must ensure the models are robust, explainable, and subject to ethical guidelines that minimize harm and maximize public benefit. Ethical AI practices, including regular audits of model performance and bias, should be adopted to ensure that technological advancements contribute positively to both environmental goals and societal equity.  
The scope of this study is confined to predictions up to 36 h ahead. Extending the forecast horizon beyond 36 h, such as 48 or 72 h, requires additional analysis and computational resources. These extended forecasts present more complex challenges due to the increasing uncertainty and variability in wind patterns over longer time frames. As a result, the development of models capable of maintaining accuracy over extended horizons necessitates further refinement in the algorithms, increased data inputs, and potentially more sophisticated computational techniques. Future work in this area should address these challenges to enhance the reliability of wind power forecasting for longer durations, which is critical for optimizing energy grid operations and ensuring efficient resource management. To conclude, all these challenges are summarized in Figure 26.  
Figure 26: Challenges and limitations of wind energy forecasting.