# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Evaluation of Weighted Fusion Methodology
#### 3.3.1 Experimental Methodology  
For each evaluation period, we generated forecasts using the rolling monthly training approach described in Section 3.1.3. The comparison focused on three forecast methodologies:  
1. Actual Load: The recorded ground truth values for grid-level load.
2. Direct Aggregation: Simple summation of provincial-level forecasts without weighting adjustments.
3. Weighted Fusion: Our proposed PCA-based integration methodology that dynamically assigns reliability-based weights to provincial forecasts.  
The evaluation metrics included time-series visualization, hourly MAPE distribution, load scatter plots with differentiated peak/non-peak periods, and provincial evaluation radar charts illustrating the three primary indicators: forecast reliability, provincial impact, and forecasting complexity.  
Figure 4: Shanghai load forecast results for July 2024.  
From the visualization in Figure 5, we can observe a comprehensive performance analysis of our forecasting methodologies across different training models. It is important to note that the Model ID represents the month used for training, where Model ID 01 indicates that January data were used to train the model, with results showing its performance in predicting February's load, and so forth in a sequential pattern.  
The top panel illustrates the overall MAPE comparison between weighted fusion and direct aggregation approaches. A striking observation is the significantly higher error rates for both methodologies when using January data to predict February load (Model ID 01), with Direct Aggregation reaching approximately 80% MAPE and weighted fusion showing about 60%. This pronounced error likely stems from the distinct load patterns between winter (January) and transition months (February), where temperature variations and seasonal consumption changes are most dramatic. As we progress through models trained on subsequent months, the error rates stabilize considerably, with weighted fusion consistently outperforming direct aggregation across all models.  
The middle panel differentiates between peak hour and non-peak hour performance, specifically for the weighted fusion methodology. Notably, non-peak hours show substantially higher error rates than peak hours for Model ID 01, approaching 80% versus 53%, respectively. This discrepancy suggests that our methodology better captures consumption patterns during high-demand periods than during transitional or low-demand periods when using January data. The performance gap between peak and non-peak hours narrows significantly for subsequent months, indicating more stable and consistent prediction capabilities as seasonal patterns become more established.  
Figure 5: Model total comparison.  
The bottom panel quantifies the percentage improvement that weighted fusion achieves over direct aggregation, broken down by overall performance and specific time periods. The improvement ranges from approximately 20% to 65%, with Models 07, 10, and 11 demonstrating the most substantial enhancements exceeding 60%. This indicates that our weighted fusion approach is particularly effective when training with data from July, October, and November to predict the subsequent months. The improvement is generally more pronounced for peak hours, underscoring our methodology's strength in capturing critical high-demand periods that are typically of greatest concern to grid operators.  
These results confirm that the weighted fusion methodology substantially outperforms direct aggregation, with the magnitude of improvement varying based on the seasonal characteristics of the training data. The method illustrates remarkable adaptability across different months, with particular strength in properly weighting provincial contributions during challenging transitional seasons and peak demand periods.