## 3 Methods
### Feature Engineering  
In power plant unit data analysis projects, conducting effective feature engineering is a crucial step towards achieving high-quality model predictions. This section delves into the feature engineering strategy implemented in this study, including encoding of categorical variables, creation of new features, and feature selection, which were all aimed at enhancing the model's predictive accuracy and interpretability.  
Figure 5: Correlation analysis chart for power plant units.  
For the encoding of categorical variables, given that \"unit type\", \"cooling method\", and \"product type\" are categorical variables within the dataset, directly utilizing these non-numeric features could potentially limit the effectiveness of certain algorithms. Hence, we employed the one-hot encoding technique to convert these categorical variables into numerical form, thereby transforming each category into a new binary feature to facilitate easier processing and understanding by the model.  
In the process of feature engineering, we identified the power supply efficiency of the unit as a key indicator, which is particularly crucial for predicting the unit's emissions. Therefore, we decided to develop a new feature named \"power supply efficiency\", which is calculated by the ratio of \"power supply\" to \"power generation\". Specifically, power supply efficiency reflects the capability to convert produced electricity into electricity that is available for end-users. High efficiency indicates optimized energy utilization, whereas low efficiency may suggest losses of energy during the conversion or transmission processes. The calculation of power supply efficiency is shown in Equation (2). Furthermore, we constructed a feature known as equivalent full load hours, which is derived by dividing the electricity generation by the installed capacity. The result of this calculation can be interpreted as the equivalent full load operating time, that is, the duration for which a power generating facility would need to operate at its maximum capacity (installed capacity) without interruption to produce the actual amount of generated electricity. Equivalent full load hours provide a means to assess the efficiency and utilization of a power generating facility, aiding in the understanding of a facility's operational performance over a certain period. The method of calculation is illustrated in Equation (3).  
\[\text{power supply efficiency}(\%)=\left(\frac{\text{power supply}}{\text{ power generation}}\right)\times 100 \tag{2}\]  
\[\text{equivalent full load hours}=\frac{\text{power generation}}{\text{ installed capacity}} \tag{3}\]  
Finally, we selected features for model training. Given that observations from the data indicate that the \"cooling method\" is \"open water cooling cycle\" for almost all units, it can be determined that this feature contributes minimally to the training effectiveness of the model, and thus it was decided to exclude it. The remaining features were retained for model training. Additionally, the two newly added features, \"power supply efficiency\" and \"equivalent full load operating hours\", were also considered, with the aim of enhancing the predictive performance of the model.