# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 7 Discussion and Challenges
### Critical Requirements for Advanced Building Energy Retrofit Modeling and Evaluation  
In the rapidly evolving field of building energy retrofits, there is an urgent need for more advanced, adaptable, and flexible data-driven approaches. These approaches, powered by machine learning (ML) and artificial intelligence (AI), promise to revolutionize how we predict and implement energy-saving measures. By focusing on creating models that are easily replicable, require specific data inputs, minimize development time, undergo rigorous verification, and maintain high predictive accuracy, we can significantly enhance the efficiency and effectiveness of retrofit projects. Based on the review conducted in this study, below is a set of critical requirements for advanced building energy retrofit modeling and evaluation. 1. Adaptability and Flexibility The models should be adaptable to a wide range of building types, from residential to commercial and industrial buildings. They should also accommodate different energy systems, including heating, ventilation, air conditioning (HVAC), lighting, and renewable energy systems. Given the variability in building usage patterns and external conditions (e.g., changing weather patterns due to climate change), the models must dynamically adapt to data inputs. 2. Optimized Data Requirements A well-designed data-driven model should clearly specify the essential data required, such as energy usage patterns, building envelope characteristics, occupancy schedules, weather data, and energy system performance metrics. This helps streamline data collection, focusing on the most impactful variables. In cases where complete data are not available, models can employ data enrichment techniques, such as synthetic data generation, interpolation, or the use of similar case studies to fill gaps. This ensures the model remains functional even with incomplete datasets. Furthermore, integration with the Internet of Things (IoT) and smart sensor networks can greatly enhance data collection, providing data on various building parameters such as temperature, humidity, occupancy, and energy consumption. These data can improve model accuracy and enable more precise predictions of energy savings. 3. Efficient and Scalable Development Leveraging automated machine learning tools can significantly reduce the time and expertise required to develop models. These tools can automatically select the most appropriate algorithms, tune hyperparameters,and even pre-process data, making model development faster and more accessible. Moreover, utilizing pre-trained models or transfer learning techniques can further expedite development. By applying knowledge from models trained on similar buildings or retrofit scenarios, one can reduce the need for extensive retraining, saving time and resources. 4. Rigorous Verification and Validation Models must undergo extensive testing across a range of scenarios and building types to ensure their reliability and accuracy. This could involve back-testing on historical data, cross-validation with different datasets, and pilot projects in real-world settings. In this regard, incorporating uncertainty quantification in the model's predictions can provide a range of potential outcomes, helping stakeholders understand the risk and variability associated with different retrofit measures. This is particularly important in scenarios where data are sparse or highly variable. 5. Enhanced Predictive Accuracy and Insights High predictive accuracy is critical, and models should be capable of making granular predictions, not just at the building level but also at the level of individual systems (e.g., HVAC and lighting). This enables more targeted interventions and maximizes the impact of retrofit measures. The models should support scenario analysis, allowing stakeholders to explore different retrofit options and their impacts.