# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 5 AI and Machine Learning Algorithms in Energy Retrofitting
\begin{table}
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**Study** & **ML Algorithm Type** & \multicolumn{2}{c}{**Specific Algorithm and Application**} \\ \hline \multirow{6}{*}{[10]} & & \(\bullet\) & Random Forest (RF) for energy prediction. \\  & & \(\bullet\) & Extreme Gradient Boosting (XGBoost) for energy prediction. \\  & & \(\bullet\) & Extra Trees (ET) for energy prediction. \\  & & \(\bullet\) & Ensemble learning methods for energy prediction. \\  & & \(\bullet\) & Artificial neural network with a multilayer perceptron (MLP) for energy prediction. \\ \hline \multirow{6}{*}{[11]} & Supervised Learning, Optimization Algorithm. & \(\bullet\) & Optimized Gradient Boosted Regression Tree (GBRT) using the SMAC algorithm to predict Building Emission Rate (BER). \\ \hline \multirow{6}{*}{[18]} & Supervised Learning, Deep Learning. & \(\bullet\) & Recurrent Neural Network (RNN) to capture the sources of uncertainty that arise in building energy simulation. \\ \hline \multirow{6}{*}{[19]} & & \(\bullet\) & Local Outlier Factor (LOF) algorithm for outlier detection. \\  & & \(\bullet\) & Greedy Optimization algorithm to determine optimal features. \\  & & \(\bullet\) & Deep Learning to compute and predict the energy rating. \\  & & \(\bullet\) & Rule Induction to compute and predict the energy rating. \\  & & \(\bullet\) & Neural Network to compute and predict the energy rating. \\  & & \(\bullet\) & Naive Bayes to compute and predict the energy rating. \\  & & \(\bullet\) & Decision Trees to compute and predict the energy rating. \\  & & \(\bullet\) & Random Forest to compute and predict the energy rating. \\  & & \(\bullet\) & Gradient Boosted Trees to compute and predict the energy rating. \\  & & \(\bullet\) & Learning Vector Quantization (LVQ) to compute and predict the energy rating. \\  & & \(\bullet\) & K-Nearest Neighbors (KNN) to compute and predict the energy rating. \\  & & \(\bullet\) & Deep Learning to compute and predict the energy rating. \\ \hline \multirow{6}{*}{[21]} & & \(\bullet\) & Isolation Forest (IF) for Data Cleansing. \\  & & \(\bullet\) & Bidirectional Recurrent Bayesian Neural Network with Fuzzy C-means Clustering (BRBNN-FCM) for performance modeling. \\ \cline{1-1}  & & \(\bullet\) & Principal Component Analysis with Trimmed Score Regression (PCA-TSR) for data imputation. \\ \hline \multirow{2}{*}{[25]} & \multirow{2}{*}{Supervised Learning.} & \(\bullet\) & Extreme Gradient Boosting (XGBoost) to estimate building energy performance (BEP) before and after retrofits. \\ \cline{1-1}  & & \(\bullet\) & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of key studies on AI and machine learning applications in energy retrofitting. Table 3 underscores the diverse application of machine learning (ML) techniques in enhancing building energy efficiency and optimizing retrofit decisions. Predominantly, studies employ supervised learning methods, such as Random Forest (RF), XGBoost (XGB),  
\begin{table}
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**Study** & **ML Algorithm Type** & \multicolumn{2}{c}{**Specific Algorithm and Application**} \\ \hline
[26] & Supervised Learning & • • •  
and Artificial Neural Networks (ANNs), which are effective in handling complex datasets and predicting energy performance. The integration of deep learning approaches, including multi-layer perceptron (MLP) and recurrent neural networks (RNNs), further improves prediction accuracy and simulation capabilities. Optimization algorithms, notably genetic algorithms (GAs) and multi-objective genetic algorithms (MOGAs), are used in conjunction with ANNs to refine retrofit solutions, while ensemble methods enhance prediction accuracy by combining multiple models. The application of these techniques spans from predicting energy savings and retrofit opportunities to improving energy databases and decision support systems, utilizing real-world data for practical relevance.