## 3 Methodology  
### Prediction Model Selection  
To select a model used for performance prediction, a data-based machine-learning model widely used in the field of building and facility systems was considered. Artificial neural network model (ANN), support vector machine model (SVM), random forest model (RF), and K-nearest neighbor model (KNN) were selected. ANN model is planned to copy the fundamental architecture of the human brain, whose essential component is called a processing unit modeling a biological neuron. ANN is composed of a multilayer perceptron structure such as an input layer, a hidden layer, and an output layer, and is a machine-learning algorithm that updates the weights between each node by learning the correlation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Input Variable**} & \multicolumn{2}{c}{**Non-Standardization Factor**} & \multicolumn{3}{c}{**ANOVA**} \\ \cline{2-6}  & **B** & **Std. Err** & **R2** & **F** & **Sig** \\ \hline Source side input temperature & 8.34 & 1.52 \(\times\) 10\({}^{-16}\) & \textless{}0.05 & 0.9023 & 8.44 & \textless{}0.001 \\ Source side output temperature & 8.50 & 0.0448 & \textless{}0.05 & 0.556 & 104.27 & \textless{}0.001 \\ Load side input temperature & 2.96 & 0.0822 & \textless{}0.05 & 0.3316 & 167.16 & \textless{}0.001 \\ Load side output temperature & 3.62 & 0.1242 & \textless{}0.05 & 0.1661 & 8.71 & \textless{}0.001 \\ \between input and output variables through back propagation learning. The artificial neural network is excellent in predicting nonlinear systems, but has the disadvantage of falling into local optimization and overfitting. The artificial neural network model proceeds in two major processes. The first is a feed-forward process, which calculates the output value using a series of input variables, variables in the hidden layer, the relationships between each variable (Connectivity, Weight) and transfer functions. The second process is the backpropagation process, which corrects the relationship between variables using the error between the output value calculated from the model and the actual value to enable accurate calculation. In the artificial neural network, each node's signal has a weight according to its importance, and receives an input signal and calculates information according to an equation. The support vector machine is a widely used machine-learning method for modeling characteristics of data and classifying data using information according to characteristics. In general, SVM is widely applied in a binary classification method that selects and classifies each data when there is data composed of two categories. This method acquires feature data from training data for constructing a classification model, and creates a model that classifies two categories through the acquired information. The classification model generated from the training data can effectively predict the category to which the data be-longs to new data that has not been used for training, and becomes a classification model that can be applied to a new problem. Random Forest analyzes and aggregates several decision trees to create a final prediction model. A forest is formed from several decision trees sampled at random. Since random forest makes independent decision trees repetitively by giving maximum randomness in sample selection and variable selection for each model, prediction errors can be reduced by lowering variance while maintaining low bias of decision trees. Even in high-dimensional data including many explanatory variables, it is stable without causing errors because the interactions and nonlinearities between explanatory variables are considered. In this method, by inputting data that deviate from the importance of the variable to the model, it is examined whether the input variable is important to the model, i.e., randomly cycled OOB data are input to the model, and the importance of variables is measured according to the equation.