# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 4 Data-Driven Approaches to Building Energy Retrofitting
### Data Sources
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Objective** & **Dataset** \\ \hline
[42] & Improve energy efficiency in this building stock. & Swedish database of Energy Performance Certificates (EPCs). \\ \hline
[43] & Develop a surrogate retrofit model that balances accuracy and computational cost. & Data on residential buildings from GIS and census sources. \\ \hline
[44] & Develop a multi-source data fusion deep learning framework to predict building energy efficiency ratings. & Energy Performance Certificate (EPC) data, the UK Buildings dataset and Google Street View (GSV) images \\ \hline
[45] & Improved retrofit ranking with machine learning pipelines. & Building characteristics data (Lombardy region). \\ \hline
[46] & Predict energy savings using ensemble learning. & De-risking Energy Efficiency Platform (DEEP) database. \\ \hline
[47] & Predict urban-scale energy performance with ensemble models. & EPCs, census data, weather, construction data. \\ \hline
[48] & Develop a data-driven framework to assess and optimize residential building retrofits. & Simulation data (HOT2000, HTAP). \\ \hline
[49] & Develop a fast multi-objective optimization method for building retrofits that accounts for future climate conditions & Metered energy use data. \\
[50] & Identify key building variables for clustering and retrofitting. & Energy audit reports for office buildings. \\ \hline
[51] & Comprehensive retrofitting framework for tropical climates. & Energy simulation data. \\ \hline
[52] & Develop different building archetypes for addressing various urban energy challenges & EPC data, heat energy use, climate data. \\ \hline
[53] & Predict building energy consumption using CatBoost. & Seattle Energy Benchmarking Program data. \\ \hline
[54] & Multi-objective optimization for industrial retrofits. & Simulation data, thermal characteristics, schedules. \\ \hline
[55] & Develop database for SME building retrofits. & EnergyPlus simulation database (DEEP). \\ \hline
[56] & Calibrated simulations for retrofitting strategies. & Energy audit and operational data. \\ \hline
[57] & Multi-model fusion for energy prediction. & Chicago building energy Performance data. \\ \hline
[58] & Develop a generalized methodology for multi-scale GIS-based mapping of building energy performance. & Energy Performance Certificate (EPC) dataset, the Irish Census dataset, the GeoDirectory database, the Irish retrofit housing scheme dataset. \\ \hline
[59] & Determine the optimal investment strategy for energy efficiency retrofits. & Data on 27 NGO buildings in Delaware, USA, including energy savings, emissions reductions, and investment costs. \\ \hline
[60] & Develop methodologies to assess building energy use and create retrofit models & Hourly wireless sensor network (WSN) data. \\ \hline
[61] & Develop a method to assess the energy performance under future climate conditions. & EnergyPlus building energy simulation data. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Table 14 presents a more detailed overview of the reviewed studies in data-driven approaches to building energy retrofitting. Overview of the key insights from Table 2:  
* Diverse Objectives: The studies aim to address a wide range of goals, from optimizing retrofit decisions on a large urban scale to developing decision support systems and AI models for enhancing building energy efficiency. Some research focuses on specific aspects, such as life-cycle optimization, real-time data utilization, or integrating explainable AI to improve model transparency and trust. * Varied Datasets: The datasets utilized across these studies are equally varied as seen in Figure 3, reflecting the different scales and contexts of the research. Commonly used datasets include Energy Performance Certificates (EPCs) from various countries (e.g., Ireland, Sweden, and the UK), building energy consumption data, and simulation-generated datasets.