# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 7 Discussion and Challenges
### Challenges and Research Gaps  
The analysis of the selected papers on data-driven energy retrofitting has revealed several critical research gaps that need to be addressed to advance the field. These gaps span a wide range of topics, from data quality and modeling challenges to practical application barriers, and they underscore the complexity of implementing effective energy retrofitting strategies on a large scale. Data Availability and Quality: A significant challenge identified in the literature is the lack of comprehensive and accurate data in existing building stock databases. Many databases suffer from outdated information, poor data quality, and insufficient physical descriptions of buildings. Moreover, there is a notable absence of detailed information on the existing and potential retrofit measures. This lack of data limits the ability to perform accurate energy performance assessments and hinders the development of reliable models for predicting retrofit outcomes. Urban-Scale Retrofitting and Contextual Considerations: The literature reveals a gap in studies focusing on urban-scale retrofitting, particularly for residential buildings. Most existing research is limited to commercial buildings or specific climates, resulting in a lack of generalized solutions applicable across different urban contexts. Furthermore, simulation-based models often struggle to account for the complex inter-building energy dynamics and the broader urban environment, leading to challenges in accurately predicting the performance of large-scale retrofits. Modeling and Simulation Challenges: There is a clear need for more adaptable and robust models that can better simulate the effects of energy retrofits. Existing models often face difficulties in capturing the interaction between multiple retrofitting measures, and the optimization objectives are frequently limited to the operating energy or life-cycle cost, rather than a more comprehensive evaluation that includes life-cycle energy and carbon impacts. Additionally, current models tend to separate the tasks of predicting energy consumption and assessing the influence of urban context, which limits their effectiveness. Practical Application and Decision-Making: The transition from theoretical models to practical applications remains a significant hurdle. There is a lack of decision support tools that can effectively incorporate decision-makers' preferences and account for the uncertainties inherent in retrofit projects, such as savings estimation and cost fluctuations. Moreover, the existing tools often rely on pre-simulated data or fixed assumptions, which may not be adaptable to the dynamic nature of building retrofitting projects. Addressing Uncertainties and Enhancing Predictive Accuracy: Many studies have highlighted the uncertainties in building energy performance evaluations, particularly when using data-driven models. The accuracy of these models is often compromised by the limited availability of high-quality data, missing data, and noise in the datasets. Expanding the Scope of Retrofit Research: Finally, the scope of current retrofit research is often limited to specific aspects of energy efficiency, neglecting important factors such as the economic feasibility of retrofits, the impact of retrofits on carbon emissions, and the integration of renewable energy systems. There is also a need for more studies that explore the application of data-driven methods in predicting retrofit outcomes at a larger urban scale, beyond individual buildings. Expanding the scope of retrofit research to include these factors will be crucial in developing more comprehensive and effective energy retrofit strategies. To better understand the current challenges and opportunities in data-driven energy retrofit, Table 1 summarizes the research gaps identified across various studies. Each entry includes a reference to the study and the specific research gap(s) it highlights.