# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 7 Discussion and Challenges  
### Discussion  
The evolution of data-driven decision support systems for energy retrofitting has seen significant advancements, particularly through the integration of artificial intelligence (AI) and machine learning (ML) techniques. These innovations have enabled more accurate energy performance predictions, streamlined retrofit processes, and addressed several longstanding challenges, such as incomplete data, scalability, and generalizability. However, despite these strides, notable research gaps still require further exploration. A primary challenge in energy retrofitting is managing incomplete or inconsistent data. In many cases, as shown in the work by [PERSON] et al. [25], the absence of granular data--such as real-time occupant behavior and operational anomalies--hampers the precision of energy-saving estimates and post-retrofit performance evaluations. [PERSON] et al. [21] use isolation forests for data cleansing and advanced ML techniques like Bayesian Ridge Building Networks (BRBNNs) and Principal Component Analysis (PCA) to impute missing data, highlighting how hybrid models can overcome gaps in datasets. Similarly, [PERSON] et al. [47] employ ensemble learning models, such as XGBoost and LightGBM, which combine multiple model predictions to deliver reliable forecasts, even when faced with incomplete datasets. This reflects an increasing trend toward developing sophisticated algorithms to handle the incomplete or inconsistent data that pervades retrofitting projects. Beyond handling incomplete data, ML models have also helped address issues of data integration. As highlighted by studies such as that of [PERSON] et al. [46], which uses ensemble learning to predict energy savings, ML algorithms can process heterogeneous data sources--such as sensor data, energy performance certificates, and weather information--combining them into cohesive inputs for retrofit planning. One of the most significant innovations in the field is the use of artificial neural networks (ANNs) and time-series models to optimize retrofit strategies. [PERSON] et al. [48] develop a surrogate ANN model that integrates real-time sensor data and historical energy consumption records to predict retrofit outcomes, demonstrating how data-driven techniques can provide cost-effective solutions even when high-resolution data are unavailable. Time-series forecasting techniques, such as weighted support vector regression (SVR) used by [PERSON] et al. [69], allow for short- and medium-term energy consumption predictions, effectively addressing the challenge of incomplete or inconsistent time-series data. These advancements make real-time monitoring and optimization a reality, allowing for more responsive and dynamic retrofit strategies. Explainable AI (XAI) has emerged as another innovation that addresses the transparency issues associated with traditional black-box ML models. [PERSON] et al. [27] use XAI techniques to make AI-driven decisions in energy retrofitting more transparent, allowing stakeholders to understand the reasoning behind the predictions. This is crucial in energy retrofitting, where building owners, policymakers, and investors need to trust the system's recommendations. XAI provides an essential bridge between sophisticated ML algorithms and their practical adoption by making the decision-making process clear and interpretable. Despite these advancements, scalability remains a persistent challenge in large-scale retrofit efforts. While models developed by [PERSON] et al. [26] demonstrate how data-driven urban energy simulations (DUE-S) can predict energy savings across multiple buildings, they also highlight the difficulties of scaling these solutions to broader urban environments. [PERSON] et al. [52] emphasize the need for archetype-based models tailored to specific building stocks and regional conditions, noting that more generalized ML models often lack the precision needed for localized retrofitting efforts.