# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 4 Data-Driven Approaches to Building Energy Retrofitting
### Data Types  
In the realm of building energy retrofits, data-driven approaches are pivotal for developing effective strategies and optimizing energy performance. Various types of data play crucial roles in informing these approaches, including energy consumption data, building characteristics, weather data, and operational data. Energy Consumption Data: Energy consumption data collected from various sensors and meters provide critical insights into consumption patterns and inefficiencies. Sensors placed throughout a building can capture granular data on parameters such as temperature, humidity, and occupancy, which further informs energy usage patterns. For example, in [PERSON] et al. [25], the authors used 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 to employ an agent-based building stock model for the German residential sector to explore the relationship between prediction accuracy and retrofit rates. Building Characteristics: These include the construction materials, insulation levels, HVAC systems, window types, and occupancy patterns, which are crucial for accurate energy modeling and simulation. Detailed building characteristics can help tailor retrofit measures to specific building types; for example, [PERSON] et al. [26] used hourly electricity consumption data for 29 buildings in downtown Sacramento, California, for the years 2016-2018, along with publicly available data on the building geometry, building characteristics, and weather data for the same area and time period, to develop a greedy optimization algorithm that can identify the minimum number of buildings that need to be retrofitted to achieve maximum energy savings across an urban area. Moreover, [PERSON] and [PERSON] [18] utilized a dataset that includes building geometries created from GIS data, and non-geometric building properties based on the U.S. Department of Energy (DOE)'s Commercial Reference Buildings to introduce an integrated simulation and data-driven method (Data-driven Urban Energy Simulation or DUE-S) to model large-scale retrofit policies. Weather Data: Local climate data include temperature, humidity, solar radiation, and wind speed, which impact building energy performance and are necessary for precise simulations and predictions. Weather data are also integral to understanding how external conditions affect energy use and can enhance the accuracy of energy models. In the work of [PERSON] et al. [27], the authors used the UNICON dataset, which is drawn from the La Trobe Energy AI/Analytics Platform (LEAP) and contains energy consumption and weather data for more than 100 buildings across multiple campuses of La Trobe University from 2018 to 2020 to develop and evaluate a robust and explainable AI-based framework for the measurement and verification (M&V) of energy conservation measures (ECMs) in buildings. Operational Data: Building operations information, such as lighting schedules, equipment usage, and maintenance activities, helps to understand and optimize energy use. Operational data provide context for energy consumption patterns, highlighting potential areas for improvement. Looking at the work of [PERSON] et al. [20], the authors developed a digital twin solution, presented in Figure 2 that utilizes building operational data and sensor data; this solution will provide decision-making support for energy retrofits through testing and simulation. Figure 2: DanRETwin solution illustration with the main technology components and connections. Reprinted from Ref. [20]. Combining these diverse data types allows for a comprehensive analysis and optimization of energy use in buildings. By integrating energy consumption data, building characteristics, weather conditions, and operational insights, stakeholders can develop more effective retrofit strategies, leading to significant improvements in building energy efficiency and performance.