# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 4 Data-Driven Approaches to Building Energy Retrofitting
### Data Sources
They are often used in conjunction with other data collection methods to provide a detailed understanding of energy use and potential savings; see the work of [PERSON] et al. [30], [PERSON] et al. [31]. Energy Modeling: Building energy modeling involves the use of simulation software to predict energy consumption and savings associated with retrofit measures. Models can simulate a range of scenarios based on different retrofit strategies, allowing for the evaluation of potential impacts on energy performance before implementation. Energy modeling is crucial for planning and optimizing retrofit interventions, as it provides predictive insights into how changes will affect overall energy use. This approach supports decision-making by forecasting the potential benefits and costs of various retrofit options; see the work of [PERSON] et al. [32]. Digital Twins: Digital Twins are virtual replicas of physical buildings that use real-time data to simulate and analyze building performance. They integrate data from sensors, BMS, and other sources to create a dynamic model that mirrors the real-world conditions of the building. Digital twins enable the comprehensive monitoring and simulation of building systems, allowing for predictive analysis and optimization of energy use. They offer a powerful tool for assessing retrofit impacts, testing scenarios, and refining strategies by providing detailed insights into how changes will affect building performance; see the work of [PERSON] et al. [20]. The combination of different data sources allows for a thorough evaluation of energy performance and the effectiveness of retrofit measures. In particular, the integration of data-driven approaches in building energy retrofitting is increasingly supported by a growing body of research that utilizes diverse datasets and objectives to enhance energy efficiency. The detailed analysis of these selected studies, extracted through a rigorous database search, reveals the breadth of data sources and methods applied across different contexts, highlighting the versatility and potential of data-driven methodologies in retrofitting processes. To provide a clearer picture of how these studies align with various objectives and datasets, we summarize the reviewed studies in Table 2. This table offers an overview of the study titles, their objectives, and the datasets used, showcasing the diversity and scope of research in this area. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Objective** & **Dataset** \\ \hline
[10] & Create a data-driven AI model to predict building energy performance for different retrofit scenarios & EPC dataset for residential buildings in the UK. \\ \hline
[11] & Develop a model for predicting Building & records of non-domestic buildings in the UK, sourced from the arbnco Consult platform. \\ \hline
[18] & Urban-scale energy modeling with hybrid approaches. & Electricity consumption, weather, building geometry. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of reviewed studies in data-driven approaches to building energy retrofitting.