## 3 Methodology  
This paper focuses only on supervised learning methods because modeling DC energy demand and ambient temperature predictions are treated as time series regression problems. To effectively model the problems, we applied Shapley Additive exPlanation (SHAP) additive feature attribution value-assisted feature selection method to identify relevant features based on various feature importance rankings. Section 3.2 presents the description and implementation procedures of the SHAP-assisted feature selection. In the feature-importance-based approach, input features are assigned a score based on how useful they are at predicting the target variables. The top-ranked features are the most significant features for modeling the specified target problem. We compared the effectiveness of the SHAP value-assisted feature selection method introduced in this paper with the following several commonly used importance-based feature selection methods: (i) Random Forest with Gini Feature Importance Ranking (RFGFRIR) (mean decrease impurity), (ii) Random Forest permutation-based Feature Importance Ranking (RFPFIR), (iii) Random Forest with SHAP-values-based Feature Importance Ranking (RFSVFIR), (iv) XGB with Gain-based Feature Importance Ranking (XGBGFRIR), (v) XGB with Permutation-based Feature Importance Ranking (XGBPFIR), and (vi) XGB with SHAP-values-based Feature ImportanceRanking (XGBSVFIR). The methods were tested and validated using a real dataset from an HPC data center, a CRESCO6 cluster consisting of 20,832 cores. The dataset was divided into a 7:3 ratio for training and testing while preserving the time order. Table 1 illustrates the dataset's features and descriptions. Then we trained the models using the first 70% of the dataset, applied the feature selection methods, and ranked the important features in prior orders. The models were retrained, being with the highest-ranked features from each feature selection method, and their performance and learning rate of the feature selection were compared. This was performed \(n-1\) times. Finally, the topmost important features with good model performance and fair computational expenses were kept. The experimental procedures are detailed in Section 3.2 in Figure 1. To validate the feature selection methods, we applied the 30% testing set of the dataset. We then retrained and compared the methods' performance and learning rate using the top 10 most important features of each feature selection method. To evaluate and compared the feature selection methods, we applied to mean absolute average error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE) evaluation criteria. The method with the lowest error and computational expenses was selected as the best method for the identification of relevant features to effectively model DC operations. In this paper, we generally adhered to the conceptual framework illustrated in Figure 1. The following three steps were systematically applied: (i) Dataset and preprocessing as described in Section 3.1, (ii) Introduction to SHAP feature attribution value-assisted relevant feature selection procedures as described in Section 3.2, and (iii) the machine learning models used to evaluate the feature selection methods presented in Section 3.3.