# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 8 Conclusions
### Future Research Directions  
Considering the identified gaps analyzed in Sections 4-6 and 7.2, it seems appropriate to suggest the following directions for further research:  
1. Improving Data Quality and Access: The lack of comprehensive, high-quality data on existing building stock remains a fundamental barrier to effective retrofitting. Future work should focus on creating centralized, standardized databases that include detailed building characteristics, historical performance data, and implemented retrofit measures. This includes leveraging advanced data collection methods such as IoT devices and sensor networks to provide real-time, granular data on building performance. Additionally, improving data sharing frameworks and protocols will ensure interoperability and accessibility, enabling the seamless integration of data from diverse sources. 2. Developing Scalable Urban Retrofitting Solutions: Urban-scale retrofitting strategies must move beyond single-building models to account for the complexities of entire districts and cities. Research should focus on developing simulation tools that integrate inter-building energy dynamics and the effects of urban environments, such as shading, heat islands, and shared resources. These tools must be scalable and adaptable to varying building typologies and climate zones, enabling the formulation of retrofit strategies that maximize energy savings while minimizing costs and disruptions. 3. Advancing Modeling and Simulation Techniques: The limitations of current models, including their inability to capture interactions between multiple retrofit measures, must be addressed. Future research should focus on creating integrated simulation frameworks that combine energy, environmental, and economic metrics. Incorporating life-cycle analyses into these models will provide a more comprehensive understanding of the long-term impacts of retrofitting measures, including operational energy savings, carbon emissions, and cost effectiveness. Real-time adaptable models, such as digital twins, should be further explored to enable continuous optimization and monitoring of retrofit strategies. 4. Enhancing Practical Decision-Making Tools: To bridge the gap between theoretical models and practical implementation, there is a need for decision-support tools that are intuitive, adaptive, and transparent. These tools should integrate real-time data inputs, incorporate stakeholder preferences, and account for uncertainties such as cost fluctuations and performance variability. explainable AI (XAI) methods should be embedded within these tools to ensure that decision-making processes are transparent and understandable to non-expert stakeholders, fostering trust and encouraging broader adoption of data-driven approaches. 5. Addressing Uncertainties and Boosting Predictive Accuracy: Uncertainties in data and model predictions hinder the reliability of retrofit strategies. Hybrid modeling approaches that combine machine learning with physics-based simulations offer a promising solution. These approaches can leverage ensemble methods to synthesize predictions from multiple models, improving accuracy and robustness. Future work should also focus on quantifying and communicating uncertainties to stakeholders, enabling more informed decision-making and reducing the perceived risks of retrofitting projects. 6. Expanding Research to Include Economic and Environmental Metrics: Retrofitting research must expand its scope to address economic feasibility, carbon reduction, and the integration of renewable energy systems. Future studies should evaluate the financial impacts of retrofit measures over their life-cycle, including operational savings and long-term value enhancements. Additionally, integrating renewable energy technologies such as solar panels, heat pumps, and energy storage systems into retrofitting strategies will support broader sustainability goals and align with climate change mitigation efforts.