## 3 Methods
### Exploratory Data Analysis  
We conducted an exploratory analysis of the 327 records selected during the data preprocessing stage, after removing 45 records of units that did not participate in production activities. First, we performed a basic statistical description of the data. Based on the fundamental statistical analysis of the power plant unit data, we find that the average installed capacity of the units is about 270.38 megawatts, with a median of 300 megawatts. This indicates that the installed capacity of most units is concentrated around this level. However, the range of installed capacities (12 to 700 megawatts) reflects a greater diversity. In terms of power generation, power supply, and heating supply, although the average values are respectively 99,212.87 kWh, 94,019.92 kWh, and 548,923.90 GJ, the distribution of the data shows a right-skewed characteristic. This means that a few higher values have raised the average, especially for heating supply, where the maximum value reached 8,533,608 GJ, indicating that some units have particularly strong heating capabilities. Subsequently, we conducted a visualization analysis of univariate data. For continuous variables such as installed capacity, power generation, power supply, heating supply, and unit emissions, we used histograms to observe their distribution. The distribution of continuous variables is shown in Figure 2, where the blue line represents the kernel density estimation (KDE) curve. For categorical variables, such as unit type and product type, bar charts are used to understand the frequency of each category. As shown in Figure 3, the distribution of categorical variables is clearly observed. Among all the types of units considered, those classified as conventional coal-fired units are significantly higher than the other types, occupying an absolute majority share. Furthermore, from the perspective of product type, the vast majority of power generation companies prefer to choose cogeneration technology as their main product form, a trend that is very common in the current field of energy  
Figure 2: Distribution of continuous variables. production. This finding not only reveals the mainstream technology trends in the current power industry, but also points to possible future directions, namely, further optimizing cogeneration technology to improve energy efficiency and reduce environmental impact. For bivariate analysis, which explores the relationship between input features and the target variable (unit emissions), scatter plots are directly used to identify the relationships between them, as shown in Figure 4. Given the close connection between power generation and power supply determined by generation efficiency, the numerical closeness of these two reveals their highly similar impact on unit emissions. In the case of heating supply, a clear proportional effect on unit emissions is observed, indicating that as heating supply increases, unit emissions also rise. In the correlation analysis of the main numerical characteristics of power plant units, the correlation graph presented in Figure 5 reveals an extremely strong positive correlation (correlation coefficient of 0.9997) between power generation and power supply. This relationship almost perfectly mirrors the increases and decreases between these variables,  
Figure 4: Scatter plot of bivariate relationships. Figure 3: Distribution of categorical variables. demonstrating a high degree of similarity in their impact on unit emissions, as shown in Figure 4. This finding aligns with the conventional understanding that as electricity generation increases, electricity supply also increases accordingly. The installed capacity also shows a strong positive correlation with the amount of electricity generated and supplied (correlation coefficients of 0.8641 and 0.8598, respectively), suggesting that units with larger installed capacity perform better in terms of electricity generation and supply.