# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## Appendix A
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & \multicolumn{2}{c}{**Reported Research Gap**} \\ \hline
[35] & \(\bullet\) & Lack of research on contextualizing the use of AI for building retrofits and understanding how AI-driven decision support systems can improve energy efficiency and stakeholder engagement. \\  & \(\bullet\) & Limitations in energy performance prediction models due to missing data and noise in the EPC dataset used for training. \\ \hline
[36] & \(\bullet\) & Previous work has focused on the perspectives of ML experts and energy experts, but there is a need to investigate XAI from the perspective of decision-makers (e.g., property owners). \\  & \(\bullet\) & Most research articles neglect evaluating XAI methods with potential users, or only emulate user evaluation, leading to inaccurate human-centered insights. \\ \hline
[37] & \(\bullet\) & Existing literature uses neural networks for regression analysis of annual building energy usage to estimate EPC labels, while this study develops ANN models to directly classify EPC labels. \\  & \(\bullet\) & ANN models are black-box models, so their internal processes and reasons for classifications are unknown, leading to reluctance and distrust in their application. This study uses explainable AI techniques to address this constraint. \\ \hline
[38] & \(\bullet\) & Lack of large-scale quantitative studies on factors influencing actual building retrofits, rather than just energy efficiency. \\  & \(\bullet\) & Lack of research exploiting the opportunities of digitization and data availability. \\  & \(\bullet\) & Lack of applications and investigations using explainable AI (XAI) techniques in energy research. \\ \hline
[39] & \(\bullet\) & Incorporating the decision maker’s preferences into the decision-making process. \\  & \(\bullet\) & Assessing the uncertainty in factors such as savings estimation, weather forecast, and retrofit action costs. \\ \hline
[40] & \(\bullet\) & Understanding the information barriers to the adoption of home energy retrofits (HERs). \\  & \(\bullet\) & Developing a system that can assist with overcoming these information barriers. \\ \hline
[41] & \(\bullet\) & Lack of a complete scientific literature on energy efficiency in wide building stocks. \\  & \(\bullet\) & Lack of a specific approach for property asset portfolios, as opposed to single buildings or urban areas. \\  & \(\bullet\) & Lack of an integrated approach that handles energy assessment, economic feasibility, decision-making, and uncertainty simulation for multiple interventions across a portfolio. \\  & \(\bullet\) & Existing methodologies are limited and heterogeneous, so new portfolio-level techniques are needed. \\  & \(\bullet\) & Need to include multi-criteria strategies to address competing economic, environmental, cultural, and social objectives. \\  & \(\bullet\) & Need to introduce decision support systems and optimization rules. \\ \hline
[42] & \(\bullet\) & Studies using Google Street View and machine learning to predict building-specific suitability for specific energy conservation measures remain a rather unexplored area of research. \\  & \(\bullet\) & There is limited knowledge on the benefits of expert influence in the generation of machine learning models outside the sphere of deep learning. \\ \hline
[43] & \(\bullet\) & Improving the performance of the surrogate model by exploring different model architectures, such as deeper neural networks, residual connections, and neural architecture search techniques. \\  & \(\bullet\) & Exploring other machine learning algorithms, such as random forests, to improve the model interpretability. \\  & \(\bullet\) & Checking the confidence of the model predictions and rejecting predictions for outlier buildings. \\  & \(\bullet\) & Extending the surrogate model to be applicable for wide-area retrofit analysis, such as at the neighborhood or city level, by accounting for regional variability and using transfer learning techniques. \\ \hline
[44] & \(\bullet\) & The uncertainty in the EPC dataset, which can lead to a gap between the estimated and actual energy performance. This can be improved in future work as European countries develop standards for EPC data quality assurance. \\  & \(\bullet\) & The limited availability of detailed building attributes outside of the EPC dataset, which constrains the broader application of the framework. The authors suggest exploring ways to balance data availability and prediction accuracy to extend the framework to buildings without EPC data. \\ \hline \hline \end{tabular}
\end{table}