# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## Appendix A
\begin{tabular}{c c c} \hline
**Study** & **Objective** & **Dataset** \\ \hline \multirow{8}{*}{[63].} & Summarize, monitor, and assess a real case of energy & This study uses three data types: 1) energy \\  & retrofitting the building envelope (external opaque walls & consumption data (thermal energy and electricity) \\  & and roof) of an existing energy-inefficient affordable & from sensors in four apartments before and after \\  & residential building. Collect quantitative data by & the retrofit; 2) indoor temperature data from \\  & monitoring four apartments to compare energy savings & sensors in three rooms (living room, bedroom 1, \\  & before and after retrofitting, alongside qualitative data on & and bedroom 2) of these apartments; and 3) \\  & occupant behavior to evaluate thermal comfort. Draw & occupant survey data on daily living behaviors \\  & conclusions from both the quantitative and qualitative & and heating/cooling usage patterns. \\ \hline \multirow{8}{*}{[64]} & Conduct a holistic cost-optimal retrofit analysis for a & Measured values of room temperature, occupancy, \\  & Swiss single-family house by identifying and collecting & and electricity demand were obtained during \\  & popular retrofit measures along with their investment & winter 2016/2017 using a wireless sensor network \\  & costs. Combine these measures into various retrofit & in the reference building. The heat demand for \\  & strategies and assess their cost effectiveness and & domestic hot water (DHW) is calculated according \\  & environmental impact. Finally, determine and discuss the & to the SIA 380/1 standard and is assumed to \\  & cost-optimal retrofit strategy. & remain constant for each retrofit strategy. \\ \hline \multirow{8}{*}{[65]} & Evaluate past retrofit savings, predict potential savings & The dataset used in this study is from the U.S. \\  & from future retrofits, and optimize retrofit decisions based & General Services Administration (GSA) portfolio \\  & on these predictions. & and includes building energy data and retrofit \\  & & records for a subset of 552 buildings. Of these, 270 \\  & & buildings have recorded retrofits, while 282 \\  & & buildings have no recorded retrofits. \\ \hline \multirow{8}{*}{[66]} & Propose a data-driven life-cycle optimization approach for & The dataset used in the study includes a historical \\  & building retrofitting, assessing the economic, energy, and & energy consumption profile, building thermal \\  & environmental performance of options to determine the & property information, historical weather data, and \\  & optimal plan that maximizes cost savings, energy & real-world life-cycle inventory data. \\  & reduction, and carbon reduction. & \\ \hline \multirow{8}{*}{[67]} & Enable market actors to assess, forecast, and quantify & The DOE's Buildings Performance Database (BPD), \\  & energy efficiency opportunities and performance risks & which contains over 750,000 existing commercial \\  & using empirical building data, provide probabilistic risk & and residential buildings compiled from over 25 \\  & analysis, and reduce transaction costs for predicting & different source datasets \\  & savings across a portfolio. & The study employs two primary datasets: a \\ \hline \multirow{8}{*}{[68]} & Provide energy advising services to building owners in & The study employs two primary datasets: a \\  & the Vasterbotten region of Sweden by developing a & questionnaire dataset based on the Theory of \\  & systematic, data-driven framework and a user-friendly & Planned Behavior (TPB) and a dataset from \\  & web platform. Integrate quantitative analysis to & Boverket covering 550,000 buildings. It focuses on \\  & emphasize relevant factors and supply preliminary & 12,624 one- and two-family houses in the \\  & information for stakeholders. & Vasterbotten region, categorized by city, year of \\  & information for stakeholders. & construction, number of households, and total \\ \hline \multirow{8}{*}{[70]} & Develop a hybrid model combining weighted support & The study utilizes two datasets: a daily energy \\  & vector regression (SVR) and differential evolution (DE) & consumption dataset for an institutional building \\  & optimization to forecast both short-term (half-hourly) and & in Singapore from 2013, comprising 261 total data \\  & medium-term (daily) energy consumption. Apply and & and below 2013 for testing), and a \\  & evaluate the model using half-hourly and daily energy & consumption data from an institutional building in \\  & Singapore. & \\ \hline \end{tabular}