## 2 Methodology
### Data Analysis and Processing  
#### 2.1.1 Data Acquisition and Correlation Analysis  
The original data were obtained from eight different building groups within a large mountainous resort hotel complex over a one-year period, with each data point including a time step at 1 h intervals. The basic information of each group is shown in Table 1. The residential group is abbreviated as Rg and the office group is abbreviated as Og. The data features for each group are divided into indoor factors, outdoor factors, and the historical heat load. Outdoor factors include the dry bulb temperature, relative humidity, solar radiation, wind speed, wind direction, atmospheric pressure, and rainfall. Indoor factors consist of the personnel occupancy rate (the ratio of the actual number of people present in a room to the maximum occupancy of that room), housing occupancy rate (the ratio of the number of rented rooms to the total number of rooms in a hotel), indoor set maximum temperature, and indoor set minimum temperature. The historical load is the actual load value for a period prior to the forecasted time step. All feature values of the data were obtained through various means. Outdoor factors were obtained from weather stations; the personnel occupancy rate was obtained through millimeter wave sensors; the room occupancy rate was directly exported from the hotel's business system; the indoor set maximum and minimum temperatures were measured by indoor return air temperature sensors; and the heat load was collected by the heat meter of each group. After data acquisition, we need to select indoor and outdoor influencing factors that have a strong correlation with the building heat load as input features for training the model. We use the Pearson correlation coefficient method for the correlation analysis, which measures the strength and direction of the linear relationship between two variables. The degree of correlation between features can be judged based on the absolute value of the coefficient. The formula for calculating the Pearson correlation coefficient is as follows:  
\[r=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt {\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}\sum\limits_{i=1}^{n}{(y_{i}- \overline{y})^{2}}}}} \tag{1}\]  
where \(r\) represents the Pearson correlation coefficient, \(x_{i}\) and \(y_{i}\) represent the values of the two features for the \(i\)-th sample, and \(\overline{x}\) and \(\overline{y}\), respectively, represent the means of the two features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Name** & **Heating Area (m2)** & \
\begin{tabular}{c} **Number of** \\ **Aboveground Floors** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Underground Floors** \\ \end{tabular} \\ \hline Rg1 & 11,407 & 4 & / \\ Rg2 & 9570 & 3 & 1 \\ Rg3 & 9637 & 4 & / \\ Rg4 & 6268 & 5 & 1 \\ Rg5 & 7104 & 5 & 1 \\ Rg6 & 8831 & 5 & 2 \\ Og1 & 5235 & 2 & / \\ Og2 & 18,402 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic information of the heating floor. The absolute value of the Pearson correlation coefficient being between 0.8 and 1.0 indicates a very strong correlation, it being between 0.6 and 0.8 indicates a strong correlation, it being between 0.4 and 0.6 indicates a moderate correlation, it being between 0.2 and 0.4 indicates a weak correlation, and it being between 0 and 0.2 indicates a very weak or no correlation. The results of the analysis using the Pearson correlation coefficient method are shown in Figure 2.  
As observed, the absolute values of the correlation coefficients for the dry bulb temperature and solar radiation are the highest, ranging between 0.6 and 0.8, indicating a strong correlation with the heat load. The relative humidity and wind speed have correlation coefficients between 0.3 and 0.4, showing a weak correlation with the heat load. However, since the data are derived from a mountain hotel, these two factors should not be directly disregarded and can be considered as secondary influencing factors.