# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 5 AI and Machine Learning Algorithms in Energy Retrofitting
[47] develop machine learning models to predicts building energy performance on a large scale using an ensemble or segregation method, and then they compare the performance of ten supervised learning techniques for each of the methods (the algorithms include XGBoost (XGB), LightGBM (LGBM), Gradient Boosting (GB), Histogrambased Gradient Boosting (HGB), Random Forest (RF), Neural Network (NN), Decision Tree (DT), Linear Regression (LR), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)), and the results indicate that incorporating segregation in the analysis improves the performance of most models, particularly XGB, LGBM, and HGB. These findings highlight the importance of considering segregation in the machine learning process to obtain more accurate predictions. Unsupervised Learning: Clustering algorithms like K-means and hierarchical clustering are utilized to segment buildings or components based on energy consumption patterns, which can identify retrofit opportunities. These methods do not require labeled data, making them useful for exploratory analysis. Looking at [PERSON] et al. [21], the authors propose an innovative approach from the perspective of being data driven to support retrofitting selection under incomplete information through performance modeling and data imputation based on the already available BPDs' big data. In their approach, Isolation Forest (IF) is chosen for the data-cleaning process of building performance datasets (BPDs) because it is an effective unsupervised learning technique for anomaly detection. As an unsupervised method, IF does not require labeled data to identify outliers. Instead, it works by isolating data points that differ significantly from the rest. This is particularly valuable in BPDs, where detecting and removing anomalies is crucial to ensure the reliability of the data and the accuracy of performance modeling. By using IF, the process can effectively filter out invalid, duplicated, or anomalous entries, leading to cleaner, more dependable data for analysis. They also utilize hybrid models like the BRBNN-FCM integrated method (this model integrates Belief Rule-Based Neural Networks (BRBNNs) with Fuzzy C-Means (FCM) clustering, combining both supervised and unsupervised machine learning techniques) to model the building's retrofitting performances based on the available building properties. It models the retrofitting performance by establishing the relationship of retrofitting and performances' prediction intervals, without being supported by generally missing data. [PERSON] et al. [19] develop a generic methodology to optimize urban-scale energy retrofit decisions for residential buildings using data-driven approaches. They implement the LOF (Local Outlier Factor) algorithm for outlier detection to processes large datasets more efficiently than the distance-based and density-based outlier detection techniques. Reinforcement Learning: This approach involves learning optimal retrofit strategies through simulation, where the model interacts with an environment and learns from the outcomes of different actions. Reinforcement learning is particularly useful for dynamic and complex systems, where traditional optimization methods may fall short. While reinforcement learning (RL) is a powerful approach for optimizing complex systems, its application in energy retrofitting is currently limited in the literature. However, RL has found significant use in optimizing building operations and systems to enhance energy efficiency. For example, RL algorithms are effectively employed in controlling heating, ventilation, and air conditioning (HVAC) systems to ensure that energy usage is minimized while maintaining comfort levels [69]. These systems learn optimal strategies by interacting with the building environment, receiving feedback in the form of energy consumption data, and adjusting their actions accordingly.