## 4 Experimental and Results Analysis  
### Dataset and Baseline Settings  
#### 4.1.1 Dataset Description and Data Preprocessing  
The OSF [27] provides power and audio recordings from various global grids, and this study utilizes multiple publicly available datasets offered by the OSF. To ensure comparable data volumes across different regions, we selected datasets based on their temporal coverage and divided them into two groups, as detailed in Table 2.  
To standardize the data format, we preprocessed the datasets from seven regions uniformly, resulting in six sets of datasets, as detailed in Table 3. The specific processing methods are as follows:  
1. Time sequence alignment: Arranging all regional datasets chronologically to preserve contextual time-related information of ENF data;
2. Format alignment: Standardizing all data to actual measured values rather than differences between the measured and reference values;
3. Missing value treatment: Replacing all \"Nan\" values with the medians of each region's entire dataset. According to the OSF's dataset description, \"Nan\" values may result from measurement device or calculation errors. Instead of simply replacing missing data with nominal values, which may introduce bias, we opted for replacing all \"Nan\" values with medians to accurately reflect each dataset's operational characteristics without affecting the main trend;
4. Time-span grouping: Grouping datasets based on time spans; longer time spans provide more contextual time-related information;
5. Sequence length division: Significant fluctuation pattern differences exist in ENFs between the daytime and nighttime due to the changes in power demand and supply. For instance, electricity demand is typically higher during the day, especially on workdays, leading to a higher frequency. At night, both the electricity demand and frequency decrease [28]. Diurnal variations in wind speed also affect ENFs; higher wind speeds occur during the day, while lower speeds occur at night [29]. To investigate the sequence length impact, we selected 1 min (60 s), 1 h (3600 s), and 1 day (86,400 s) as the dataset sequence lengths--each group was divided into sequences of 60, 3600, and 86,400, respectively;  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time Span** & **Location of Measurements** & **Synchronous Area** & **Time Range of Dataset** & **Selected Range** & **Frequency** & **Resolution** & **Number of Days** \\ \hline \multirow{4}{*}{1 year} & Baden-Würtemberg, & \multirow{2}{*}{TransnetBW} & 2011-07–03 & 2018-01-01-231 & 50 Hz & 1 s & 360 \\  & Germany & & 2020-03 & 2019-12-31 & 2018-01-01-231 & 50 Hz & 1 s & 360 \\  & London, United Kingdom & \multirow{2}{*}{National Grid} & 2014-01–2019-12 & 2019-12-31 & 50 Hz & 1 s & 360 \\  & Zealand, Denmark & \multirow{2}{*}{Nordic Grid} & 2018-01-01-2018-01-231 & 2018-01-01-231 & 50 Hz & 1 s & 360 \\ \hline \multirow{4}{*}{41 days} & Karlsruhe, Germany & \multirow{2}{*}{Continental Europe} & 2019-07-09–2019-08-18 & 2019-08-18 & 50 Hz & 1 s & 41 \\  & Oldenburg, Germany & \multirow{2}{*}{Continental Europe} & 2019-07-10–2019-07-10–2019-08-07 & 50 Hz & 1 s & 41 \\  & Istanbul, Turkey & \multirow{2}{*}{Continental Europe} & 2019-07-09–2019-07-09–2019-08-18 & 50 Hz & 1 s & 41 \\  & Lisbon, Portugal & \multirow{2}{*}{Continental Europe} & 2019-07-09–2019-08-16 & 2019-08-16 & 50 Hz & 1 s & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Original dataset.  
Training set and test set divisions: Each group was split into training and test sets at an 8:2 ratio.