# 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
[26] & \(\bullet\) & Simulation-based models struggle to account for inter-building energy dynamics and urban context effect. Lack of accurate characterization of how large-scale retrofits may perform in an urban area, leading to unintended consequences. Purely data-driven approaches to predicting energy consumption and estimating the influence of urban context have been considered separate tasks, requiring more integration. \\ \hline
[27] & \(\bullet\) & The short time interval for ECM monitoring could contain biased consumption data relating to an event of significance or an outlier, and this needs to be factored in as the time interval is incrementally expanded. The prediction horizon and prediction uncertainty should be incorporated into the model development as further parameters to be fine-tuned. \\ \hline
[28] & \(\bullet\) & It is difficult to identify specific energy savings potential and retrofit measures for high-performance buildings that already use energy efficient technologies and design strategies. Previous studies have lacked comprehensive and detailed monitored data, and have focused on limited aspects of building energy performance or building systems. \\ \hline
[29] & \(\bullet\) & The CPU time required for the BEBM constructor is long, which may discourage users from using this option. The optimal simulation length for the BEBM constructor is not known and would require further research. More advanced stopping criteria for the genetic algorithm used in the BEBM constructor could potentially reduce the execution time, but this would require further research. \\ \hline
[30] & \(\bullet\) & The knowledge gap between current archetype development methods and obtaining data from the building stock to compose the archetypes. The uncertainties in archetypes that jeopardize the wide application of benchmarking and limit regional applications like UBEM simulations. \\ \hline
[31] & \(\bullet\) & Improving the quality of data-driven ECM recommendation models by incorporating new audit data and developing models for more specific ECMs or building types. Improving the data collection process by restricting numerical fields to numbers, converting open text fields to categorical fields, and defining categories based on ASHRAE standards, energy consultant input, and observed data. \\ \(\bullet\) & Expanding audit requirements to include tenant spaces, which represent a significant portion of energy use in multi-tenanted buildings. \\ \(\bullet\) & Expanding the coverage of the Greener, Greater Buildings Plan to include smaller buildings, increasing the sample size for analysis and model performance. \\ \hline
[32] & \(\bullet\) & Developing deterministic decision models for selecting cost-effective sustainable building retrofit measures for high-rise residential buildings in different climatic zones. \\ \hline
[33] & \(\bullet\) & Estimating the actual energy savings achieved after implementing various retrofit strategies. Using more suitable statistical distributions to describe the primal energy demand in clusters, rather than a Gaussian distribution. \\ \(\bullet\) & Assigning the target primal energy demand to assets in the target cluster, particularly when there are many records, to avoid underestimating the total savings. \\ \hline
[34] & \(\bullet\) & Improving techniques for handling missing data in the data pre-processing stage. \\ \(\bullet\) & Shifting from nomenclature-based to data-driven building segmentation to improve the relevance of archetype buildings. \\ \(\bullet\) & Automating the building energy simulation stage and improving model resolution by accounting for individual building geometry. \\ \(\bullet\) & Incorporating more advanced urban climate modeling to improve the overall accuracy of the model. \\ \(\bullet\) & Adopting a more holistic life-cycle perspective on the building retrofitting process, including the embodied energy and emissions of the renovation process itself. \\ \hline \hline \end{tabular}
\end{table}