# 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** & **Objective** & **Dataset** \\ \hline \multirow{4}{*}{[10]} & Create a data-driven AI model to predict building energy performance for different retrofit scenarios, using four machine learning models (XGBoost, random forest, Extra Trees, and ANN-MLP) and develop an interface for analyzing retrofit impacts. & Energy Performance Certificate dataset for residential buildings in the UK \\ \cline{1-1} \cline{2-3}  & Develop a model for predicting Building Emission Rate (BER) to estimate non-domestic building energy efficiency in the UK, provide a rapid tool for the multi-objective optimization of retrofits, use a comprehensive dataset of existing buildings, and perform sensitivity analysis to identify the impact of each input parameter on building performance. & The dataset used in the study included \\ \cline{1-1} \cline{2-3}  & Evaluate the feasibility of a Data-driven Urban Energy Simulation (DUE-S) model for rapid large-scale retrofit assessments and demonstrate the benefits of integrating data-driven and physics-based approaches for urban energy modeling. & The dataset used in the study uncombasses three years (2015–2017) of 15-minute interval electricity consumption data for 52 buildings, historical hourly weather data from NOAA for the same period, building geometries derived from GIS data, and non-geometric building properties based on the DOE’s Commercial Reference Buildings. \\ \cline{1-1} \cline{2-3}  & & \\ \cline{  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Objective** & **Dataset** \\ \hline \multirow{4}{*}{[19]} & Develop a data-driven methodology to optimize urban-scale energy retrofits for residential buildings by identifying key variables influencing energy performance and providing cost-effective retrofit recommendations. & The Energy Performance Certificate (EPC) dataset for Irish residential buildings, published by the Sustainable Energy Authority of Ireland (SEAI), the building census dataset from the Central Statistics Office (CSO) of Ireland, and the retrofit cost dataset with financial details of retrofit projects, also from SEAI. \\ \hline \multirow{4}{*}{[20]} & Design, develop, and demonstrate the “DanRETwin” digital twin solution to optimize decision-making, enable retro-commissioning, and enhance performance in energy retrofits for non-residential buildings. & a digital twin solution that will utilize building operational data and sensor data. \\ \hline \multirow{4}{*}{[21]} & Propose a data-driven approach for building retrofitting decisions under incomplete information, using BRBNN-FCM to model relationships between building properties, retrofitting measures, and performance, and PCA-TSR to impute missing data. & The Energy Performance Certificate (EPC) dataset from Sweden. \\ \hline \multirow{4}{*}{[23]} & Develop a model to evaluate the impact of retrofit technologies on stakeholder expectations, prioritize these technologies to aid decision-making, and integrate the findings with the existing EPC recommendation report. & The study involved two surveys: the first surveyed Romanian owners and tenants to understand their retrofitting requirements, and the second surveyed experts (Accredited Energy Auditors) to explore the relationship between retrofit technologies and stakeholder requirements. \\ \hline \multirow{4}{*}{[25]} & Determine how building energy performance prediction accuracy affects retrofit rates and resulting CO\({}_{2}\) emissions in the residential building sector and to design an agent-based building stock model to derive this relationship. & The real-world dataset of 25,000 single and two-family buildings from Germany, with 74 variables related to building characteristics, heating systems, and annual metered thermal energy consumption. \\ \hline \multirow{4}{*}{[26]} & Enhance the DUE-S model by improving its deep learning architecture for time-series data and expanding its ability to estimate large-scale retrofit impacts across different spatiotemporal scales in a city.