## 3 Experiment  
### Dataset and Preprocessing  
The dataset used in this study was collected from an IDC equipment setup in a laboratory environment, as illustrated in Figure 2 (additional supporting data are provided in the Supplementary Materials).  
The dataset spans from October 17 to November 16 and includes operational metrics collected by internal sensors, data acquisition devices, and external environmental monitoring units. The raw data contain nearly 16 types of measurements, including voltage, current, active power, reactive power, active energy, cabinet temperature, air conditioning return air temperature, and humidity.  
Based on practical modeling requirements, six key features--active energy, active power, reactive power, cabinet temperature, return air temperature, and humidity--are selected as input variables for model training. These features are used for both energy consumption trend prediction and operational status classification. Some representative time series are shown in Figure 3, including plots of power and environmental indicators.  
During the exploratory data analysis phase, two main categories of outliers were identified:  
1. Static anomalies: Certain temperature sensors reported constant values over extended periods, which contradicts normal operational behavior. To detect these, we applied  
Figure 3: Model training data visualizations: (**a**) Active power. (**b**) Reactive power. (**c**) Apparent power. (**d**) Air-conditioner return air temperature. (**e**) Air-conditioner return air humidity. (**f**) Cabinet temperature (Zone A).  
Figure 2: IDC equipment data collection site: (**a**) Internal view of the IDC equipment. (**b**) Real-time data collector.  
a sliding window of length \(k=4\) (i.e., covering three consecutive samples, corresponding to 20 min due to the 5 min sampling interval). If, within any window, the maximum and minimum readings differ by less than \(\Delta T=0.01\,^{\circ}\mathrm{C}\), all points in that window are flagged as static anomalies.
2. Range violations: Any temperature reading outside the plausible physical range \(T\
otin[-20\,^{\circ}\mathrm{C},\ 80\,^{\circ}\mathrm{C}]\) was classified as a range anomaly. Such extreme values are rarely observed under normal IDC operating conditions.  
To refine the filtering process, we focused specifically on sensor readings from cabinet Zones A, B, and C, where persistent zero-variance or out-of-range values occurred most frequently. After identifying all anomalous points, we applied forward-backward linear interpolation to fill short gaps with lengths up to two samples. Longer segments containing anomalies were removed entirely from the dataset.  
Finally, the cleaned data were aligned to uniform 5 min intervals by rounding each timestamp down to the nearest 5 min mark, resulting in a temporally consistent time series suitable for subsequent modeling.  
During this preprocessing stage, it was also observed that certain temperature readings exhibited persistent flat-line behavior across multiple time points, as shown in Figure 4. Such patterns are inconsistent with expected equipment dynamics and may indicate sensor malfunctions or communication errors. These static anomalies were filtered based on duration thresholds and spatial criteria.