# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Relationship Between Evaluation Indicators and Forecasting Performance
#### 3.5.2 Time-Varying Weight Dynamics  
The dynamic adaptability of our weighting approach is demonstrated in Figures 19 and 20, which track the evolution of provincial weights over time. Figure 19 reveals subtle but important differences between peak and non-peak weights for Shanghai over a 48 h period. While both weight profiles follow similar trajectories, peak weights consistently remain lower during daytime hours (08:00-18:00) and higher during early morning hours (00:00-06:00) in juxtaposition with non-peak weights. This temporal pattern aligns with Shanghai's urban load characteristics, where commercial and residential air conditioning creates more volatile and less predictable load patterns during daytime peak hours. During early morning hours, when industrial loads dominate, the forecasting reliability improves, resulting in higher weights. The convergence of peak and non-peak weights around 06:00 and 23:00 marks the transition periods between these distinct operational regimes. Figure 20 compares the time-varying weights for Shanghai and Jiangsu over the same 48 h period, revealing significantly higher weights assigned to Jiangsu throughout the observation window. This substantial difference reflects Jiangsu's stronger overall evaluation metrics, particularly its higher provincial impact score due to its large industrial base. The weight trajectories also demonstrate important temporal differences: while  
Figure 17: Comparison of peak and non-peak metrics. Figure 18: Comparison of two regions’ metrics. Shanghai's weights remain relatively stable with minor fluctuations, Jiangsu exhibits a more pronounced upward trend, increasing from 0.257 to 0.282 over the 48 h period. This divergence in weight evolution patterns reflects the different responses of each province to changing operational conditions. Jiangsu's steadily increasing weight indicates improving forecast reliability as operational conditions stabilize, while Shanghai's more stable weight profile suggests less sensitivity to these changing conditions. These distinct patterns validate the importance of our time-varying weighting approach, which captures not only the relative strengths of different provinces but also their dynamic responses to evolving grid conditions. Figure 19: Time-varying weights between peak and non-peak. Figure 20: Two regions’ time-varying weight divergence. June (Moderate-Temperature Conditions)  
In June, the weight distribution shows a clear hierarchical pattern, with Jiangsu receiving the highest weights (0.30-0.35), followed by Zhejiang (0.25-0.30), and substantially lower weights for Shanghai, Anhui, and Fujian (0.10-0.15). This weight distribution corresponds with moderate MAPE values (3-9%) during peak hours (8:00-16:00). The relatively stable weights assigned to each province throughout the day indicate consistent forecasting reliability under moderate-temperature conditions. Notably, the weighted fusion approach exhibits particular effectiveness during afternoon peak hours (12:00-16:00), where it reduces MAPE by approximately 6% compared to direct aggregation. August (High-Temperature Conditions)  
In August, we observe a similar hierarchical pattern in provincial weights but with increased temporal variations, particularly for Jiangsu and Zhejiang. Jiangsu's weights show more pronounced fluctuations (0.28-0.37) throughout the day, while Shanghai's weights remain consistently low. This period is characterized by significantly higher MAPE values for both methodologies (weighted fusion: 5-7%; direct aggregation: 15-18% during peak hours), reflecting the increased forecasting difficulty during extreme high temperature conditions when air conditioning loads create volatile consumption patterns. The substantial performance gap between weighted fusion and direct aggregation (approximately 10-12% MAPE reduction) demonstrates the critical importance of dynamic weight adjustments during challenging seasonal conditions.