# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 4 Data-Driven Approaches to Building Energy Retrofitting
### Data Sources
Several studies also incorporate weather data, socio-demographic information, and other contextual data such as building geometry or historical data. * Geographical Diversity: The research spans multiple geographic regions, including Europe (Ireland, Sweden, Germany, Italy, and the UK), North America (California and New York), and China, among others. This highlights the global relevance of energy retrofitting research and the need to tailor approaches to specific regional conditions. * Integration of Advanced Technologies: A significant number of studies focus on the integration of advanced technologies like AI, machine learning, and digital twins into the retrofitting process. These technologies are employed to enhance predictive accuracy, optimize retrofit solutions, and enable more informed decision-making processes. * Policy Implications: Some studies aim to support policy-making by providing data-driven tools and indices, such as the Energy Retrofit Index (ERi) or frameworks for policy impact analysis. This suggests an increasing emphasis on aligning research outcomes with practical applications in energy policy and building regulations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Objective** & **Dataset** \\ \hline
[62] &
\begin{tabular}{c} Create a data-driven methodology to \\ verify energy efficiency savings in \\ commercial buildings. \\ \end{tabular} & EnergyPlus simulations, monitoring data. \\ \hline
[63] & \
\begin{tabular}{c} Assess retrofitting impacts in residential \\ buildings. \\ \end{tabular} &
\begin{tabular}{c} Energy consumption, indoor \\ temperature, occupant surveys. \\ \end{tabular} \\ \hline
[64] & \
\begin{tabular}{c} Conduct a holistic cost–optimal retrofit \\ analysis \\ \end{tabular} &
\begin{tabular}{c} Measured values of various temperature \\ sensors and energy consumption. \\ \end{tabular} \\ \hline
[65] &
\begin{tabular}{c} Predict energy savings from retrofitting \\ decisions. \\ \end{tabular} & GSA building portfolio data. \\ \hline
[66] & \
\begin{tabular}{c} Life-cycle optimization for retrofit \\ planning. \\ \end{tabular} &
\begin{tabular}{c} Energy consumption, thermal property, \\ life-cycle data. \\ \end{tabular} \\ \hline
[67] & \
\begin{tabular}{c} Enable market actors to assess, forecast, \\ and quantify energy efficiency \\ opportunities and performance risks \\ \end{tabular} &
\begin{tabular}{c} The DOE Buildings Performance \\ Database (BPD). \\ \end{tabular} \\ \hline
[68] &
\begin{tabular}{c} Provide energy advising using systematic \\ frameworks. \\ \end{tabular} & Survey data, building stock data. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. Overall, the reviewed studies collectively advance the field of building energy retrofitting by employing data-driven methods to optimize retrofitting strategies, enhance energy efficiency, and support policy and decision-making processes. The research underscores the importance of integrating diverse data sources, employing cutting-edge technologies, and considering regional contexts to achieve sustainable retrofitting outcomes. While data-driven approaches have shown significant promise in advancing building energy retrofits, several limitations remain evident in the reviewed studies. Many works lack a consideration of the urban-scale retrofitting challenges, such as integrating interbuilding energy dynamics and broader urban context effects. For example, while [PERSON] et al. [34] explore city-scale retrofit strategies using district heating data, their methodology could be expanded to include multi-building interactions and energy-sharing opportunities. Additionally, the long-term impacts, such as life-cycle energy and carbon assessments, remain underexplored. [PERSON] et al. [47] present a promising urban-scale model using ensemble learning techniques, yet the approach would benefit from life-cycle analysis integration to enhance decision-making. The scalability of models across diverse building types and regions is another key challenge. [PERSON] et al.