# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## Appendix A

Table 15: Cont. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & \multicolumn{2}{c}{**Reported Research Gap**} \\ \hline  & \(\bullet\) & The economic aspect of building energy retrofit was not included in the proposed ERi measure, as it is highly dependent on individual owners’ perceptions. \\  & \(\bullet\) & The economic aspect of building energy retrofit was not included in the proposed ERi measure, as it is highly dependent on individual owners’ perceptions. \\
[45] & \(\bullet\) & The framework could be extended to include the economic aspect by merging the ERi with probability theory to create retrofit scenarios. \\  & \(\bullet\) & The framework could be further validated using EPC databases from other regions/countries that use different methodologies for ranking building energy performance. \\  & \(\bullet\) & The framework could be extended to handle high-dimensional data and reconfigure the energy retrofit labels to cover cooling, lighting, and domestic hot water energy consumption, in addition to heating. \\ \hline  & \(\bullet\) & Combining physical modeling and data-driven modeling techniques to develop more accurate and comprehensive methods for estimating energy savings. \\
[46] & \(\bullet\) & Organizing a structured process to collect and verify data on EE measures, creating a larger and more diverse database. \\  & \(\bullet\) & Linking forecast error with the potential uncertainty of EE investments to support decision-making. \\  & \(\bullet\) & Developing a predictions-as-a-service, web-based tool to support stakeholders in EE financing. \\ \hline  & \(\bullet\) & More studies are needed on using data-driven models to predict energy consumption at a larger urban scale, beyond just individual buildings. \\  & \(\bullet\) & There is a lack of high-quality data in sufficient quantities to effectively train prediction models at an urban scale. \\
[47] & \(\bullet\) & Previous research has been limited by considering only a small set of parameters in predicting building energy consumption. \\  & \(\bullet\) & Fewer recent studies have incorporated crucial factors like U-values, HVAC systems, and renewable energy systems into their machine learning models. \\ \hline  & \(\bullet\) & Previous studies have not considered the carbon emission and cost impacts when making retrofit decisions for Canadian residential buildings. \\
[48] & \(\bullet\) & There is a need for prediction models for retrofit scenarios of existing residential buildings in Canada, as over 50\% of Canadian residential buildings are over 30 years old and need energy retrofits to reduce carbon emissions. \\ \hline  & \(\bullet\) & Challenges in evaluating the objective function during optimization across multiple factors such as energy performance, thermal comfort, and investment. \\
[49] & \(\bullet\) & Difficulty in generalizing results from archetypical buildings to individual cases. \\  & \(\bullet\) & Insufficient decision-making support for users in both deterministic and non-dominated optimization methods. \\  & \(\bullet\) & Need for life-cycle cost analysis that considers future climate uncertainties. \\ \hline  & \(\bullet\) & Existing methodologies do not use the typical consumption patterns detected in the analyzed facilities as a predictor variable in the energy baseline model. \\
[62] & \(\bullet\) & Existing methodologies only use data that precedes the energy efficiency measure (EEM) implementation to train the energy baseline model, which causes the loss of valuable information about how energy consumption fluctuates due to outdoor climate variables in the post-EEM period. \\ \hline  & \(\bullet\) & The current literature on using data-driven methods to predict retrofit savings is limited, with the field being dominated by simulation-based methods. \\
[65] & \(\bullet\) & This study extends the existing research on data-driven methods for predicting retrofit effects to the commercial building sector, with a focus on weather and climate factors rather than occupant characteristics.