## 2 Methodology
### Data Analysis and Processing
#### 2.1.2 Data Analysis  
Short-term building heat load data are among the key datasets reflecting changes in building energy consumption. Essentially, building heat load data are a type of time series data. Observing the heat load distribution changes of the eight groups in Figure 3, it is evident that the patterns of change are extremely complex. Time series analysis can reveal changes in trends, seasonality, randomness, and other characteristics. These analytical results can guide data preprocessing and the construction of predictive model structures, thereby improving model prediction outcomes and providing data support for energy-saving measures. Therefore, analyzing the time series characteristics of short-term building heat load data, such as trends, seasonality, and randomness, plays a crucial role. Figure 2: Correlation Analysis Results. A trend refers to the direction of change in data over time. Trends can be upward, downward, or stable, and they can be linear or nonlinear, among other characteristics. In this paper, the trends of each group are separated using seasonal decomposition, as shown in Figure 4. Upon observation, it is evident that the trends of the various groups exhibit nonlinear changes, generally rising before falling. This pattern of an increase followed by a decrease is also present in local areas, making the trend change patterns complex. 2. Seasonal Analysis  
Figure 4: Trend Graphs for Each Group. Figure 3: Heat load distribution changes of eight groups. Seasonality refers to the fixed changes present in time series data, often related to the weather, holidays, and other seasonal factors. These changes recur annually, monthly, weekly, or daily with relatively stable patterns--for example, an increase in the heating demand during winter, the changing of seasons throughout the year, weekends off, sunrises and sunsets, etc. Methods for identifying seasonality include seasonal decomposition, the seasonal index method, and the time series graphical method, among others. This paper identifies seasonality through seasonal decomposition, with the decomposition results shown in Figure 5. It is evident that each group exhibits a fixed daily heat load change pattern, characterized by a distinct 24 h seasonal variation. 3 Randomness Analysis  
Randomness refers to the random fluctuations and uncertainties in the data. The residuals of the heat load data can be decomposed by the seasonal decomposition method, and these residuals are random, which can reflect the randomness of the heat load to a certain extent, as shown in Figure 6. This randomness makes the time series data present irregular and unpredictable characteristics. There are many reasons for the randomness of short-term heat load data, including external factors, internal factors, data acquisition errors, and other unknown factors. We need to analyze the various reasons and find corresponding countermeasures to minimize the influence of randomness. For external factors, such as weather and climate changes, you can analyze the correlation between the weather data and the heat load, find the weather factors that have a large correlation coefficient with the heat load, and integrate them with the heat load data. For internal factors, such as the structure of the building itself, the use of the building, the activities of the people inside, etc., which also need to be taken into account, some quantifiable factors can be integrated with the heat load data. For the missing or abnormal data caused by data acquisition errors, the data can be supplemented or smoothed by data preprocessing. When choosing to build a prediction model, we should choose to build a model with better stability, and instead of using a single model, we can use a hybrid model to increase the robustness of the model.