# 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
[10] & \(\bullet\) & Integrating additional factors like control systems and occupant behavior into the predictive models to improve the comprehensiveness and precision of energy consumption forecasts. \\ \hline
[11] & \(\bullet\) & The size and complexity of non-domestic buildings make it challenging to identify optimal retrofit packages. The vast retrofit solution space and high time complexity of energy simulations inhibit the application of artificial intelligence in the retrofit strategy design process. \\ \(\bullet\) & Achieving comprehensive retrofit planning considering all available technologies and energy policies is not practically possible without a fast and stable energy performance emulator. \\ \hline
[18] & \(\bullet\) & Simulation-based methods are limited in their ability to quickly evaluate the effects of various design or retrofit scenarios. \\ \(\bullet\) & Data-driven methods lack an underlying physics-based engine, limiting their applicability and interpretability for assessing design or retrofit scenarios. \\ \(\bullet\) & Urban building energy models (UBEMs) have limited ability to estimate the impacts a retrofit made in one building could have on the energy use of surrounding ones. \\ \(\bullet\) & Developing and calibrating an accurate UBEM requires a considerable amount of time and computational resources. \\ \(\bullet\) & Data-driven methods require training data to understand how different energy conservation measures (ECMs) will impact future building energy use. \\ \hline
[19] & \(\bullet\) & Lack of physical descriptions for buildings in existing building stock databases. \\ \(\bullet\) & Dated information in existing building stock databases. \\ \(\bullet\) & Lack of data quality in existing building stock databases. \\ \(\bullet\) & Lack of information on existing and suggested retrofit measures in existing building stock databases. \\ \(\bullet\) & Limited studies on urban-scale retrofitting for residential buildings, with most focusing on commercial buildings. \\ \(\bullet\) & Existing studies on urban-scale retrofitting are limited to specific climates or pre-defined scenarios, and a more generalized solution is needed. \\ \hline
[20] & \(\bullet\) & Lack of systematic design and assessment tools for building energy retrofits in Denmark, especially for non-residential buildings. \\ \(\bullet\) & Existing tools rely on pre-simulated data, energy certificates, or fixed assumptions, and cannot scale up and expedite the rate of retrofit applications while maintaining accuracy. \\ \(\bullet\) & Lack of emphasis on commissioning existing buildings after retrofitting, leading to undetected faults and malfunctioning systems. \\ \hline
[21] & \(\bullet\) & Older buildings often lack complete information for building performance simulation (BPS) methods, such as missing U-values of building components due to incomplete documentation or deterioration over time. \\ \(\bullet\) & Buildings can also have case-specific incomplete information due to different documentation systems. \\ \(\bullet\) & Previous studies have tried to address incomplete information by using probability distributions based on macro-level data, but this is challenging because it is difficult to determine an objective probability distribution, and different buildings can have different missing information. \\ \(\bullet\) & Applying the same probability distribution to all buildings is unreliable, as the actual missing values are heavily influenced by building-specific characteristics. \\ \hline
[23] & \(\bullet\) & Lack of information on retrofit technologies and their benefits, which triggers stakeholder opposition to retrofit actions. \\ \(\bullet\) & Need to develop a model that evaluates the impact of different retrofit technologies on stakeholder expectations for retrofit actions. \\ \(\bullet\) & Need to improve the quality of the Energy Performance Certificate by reflecting stakeholder opinions combined with sustainable concepts to achieve significant energy savings. \\ \hline
[25] & \(\bullet\) & The relationship between building energy performance (BEP) prediction accuracy and retrofit rates, as well as the resulting CO\({}_{2}\) emission reduction potential, is not yet determined in the literature. \\ \hline \hline \end{tabular}
\end{table}