# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 6 Explainable AI for Energy Retrofitting  
Explainable AI (XAI) is an emerging field within artificial intelligence that focuses on making the decision-making processes of AI systems more transparent and understandable to humans. In the context of building energy retrofitting, XAI is crucial for ensuring that stakeholders, including building owners, engineers, and policymakers, can trust and effectively use AI-driven insights to make informed decisions Figure 5 shows Explainable AI in th retrofit decision process. By providing clear explanations of how AI models arrive at their conclusions, XAI helps bridge the gap between complex algorithms and practical application, enhancing both the adoption and impact of AI technologies in energy retrofitting. The importance of explainable AI (XAI) in building energy retrofitting lies in its ability to foster trust and transparency by clarifying how AI models make decisions, thus enabling stakeholders to understand and rely on AI-driven recommendations. XAI enhances decision-making by providing detailed insights into how various factors influence energy performance, allowing for more tailored and effective retrofit measures. It also supports regulatory compliance by ensuring that AI decisions are transparent and meet legal and funding requirements. Furthermore, XAI facilitates error diagnosis and model improvement by enabling engineers to identify and correct issues, refine algorithms, and improve overall model accuracy and robustness. In this review, we found five studies from the total reviewed studies that leverage explainable AI (XAI) to enhance various aspects of building energy retrofitting. Each study employs different XAI methods and models, showcasing the broad applications and benefits of XAI in this domain. In the work of [PERSON] et al. [36], the authors explore a range of XAI techniques, including Partial Dependency Plots (PDPs), Accumulated Local Effects (AMEs), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAPs), applied to Linear Regression, Decision Trees, and QLattice models for predicting  
Figure 5: Explainable AI in retrofit decision-making. energy consumption in residential buildings. This investigation underscores the inherent trade-off between prediction accuracy and explainability, revealing how the choice of XAI method can mitigate this trade-off and improve decision-making processes for building retrofits. They also conduct an online survey to evaluate the explainability of the models and model-XAI-method combinations from the perspective of non-ML and non-energy experts (i.e., property owners). In contrast, [PERSON] et al. [37] focus on the classification of building energy performance certificates (EPC) using an Artificial Neural Network (ANN), enhanced by LIME and SHAP. This research demonstrates the utility of XAI in simplifying complex ANN models by clarifying feature contributions, thus fostering greater trust and understanding of the model's classifications. The emphasis here is on improving transparency and trust in the predictive capabilities of ANN models. [PERSON] et al. [38] integrate supervised learning with XAI by applying Extreme Gradient Boosting (XGBoost) and SHAP to analyze retrofitting practices within the UK's residential sector. This study illustrates how XAI can validate and interpret predictive models, providing valuable insights for policy development and improving retrofit strategies. It highlights the role of XAI in transforming qualitative insights into quantitative analyses and policy recommendations. Looking at [PERSON] et al. [27], the authors present a comprehensive AI framework for the measurement and verification (M&V) of energy conservation measures (ECMs), using XGBoost and SHAP for predictive modeling and explanation. This research emphasizes the development of robust and transparent methods for evaluating energy savings, with practical applications in real-world settings.