# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Multi-Temporal Scale Analysis  
To further evaluate the adaptability and robustness of our weighted fusion methodology, we conducted an additional experiment analyzing prediction accuracy across multiple temporal scales. This experiment assessed the performance degradation of both weighted fusion and direct aggregation as the forecasting horizon extended from ultra-short-term (1 h) to medium-term (168 h/one week) predictions. Figures 13-15 show visualizations of the MAPE results across five temporal scales (1, 6, 24, 72, and 168 h) for our three evaluation periods. Figure 16a displays the moderate season (June) forecast accuracy across temporal scales. The weighted fusion methodology maintains significantly superior performance at all prediction horizons, with MAPE values ranging from 1.64% (1 h ahead) to 2.51% (168 h ahead). In contrast, direct aggregation exhibits substantially higher error rates, ranging from 5.18% to 5.82%. Interestingly, both methodologies demonstrate their lowest MAPE values at the 72 h horizon, with weighted fusion achieving 1.64% and direct aggregation achieving 4.58%. This unexpected improvement at the 72 h mark likely reflects the weekly cyclical patterns in the load data, where the 72 h forecast benefits from capturing a similar point in the weekly cycle. Figure 16b presents performance under high-temperature conditions (August), where the advantage of weighted fusion becomes even more pronounced. The direct aggregation method exhibits extremely high MAPE values between 8.0 and 9.1% across all time horizons, with particularly poor performance at the 24 h and 72 h marks. The weighted fusion approach maintains substantially better performance, with MAPE values declining gradually from 2.94% at the 1 h horizon to 2.32% at the 168 h horizon. This counterintuitive improvement at longer horizons suggests that the weighted fusion methodology successfully captures long-term seasonal patterns that compensate for the loss of short-term accuracy. Figure 16c demonstrates forecast performance during winter conditions (December), where both methods achieve their best overall accuracy. The weighted fusion approach delivers exceptional performance, with MAPE values below 1.0% for all horizons beyond 24 h. Direct aggregation shows substantial improvement as opposed to other seasons but still maintains MAPE values 2-3 times higher than the weighted fusion approach. The consistent pattern across all three evaluation periods is that the performance gap between weighted fusion and direct aggregation tends to be largest at shorter time horizons (1-24 h) and slightly narrows at extended forecasting horizons. This multi-temporal scale analysis yields three significant insights:  
1. Seasonal Differentiation : The performance advantage of weighted fusion is most pronounced during extreme temperature conditions (August), moderately significant during mild conditions (June), and least pronounced (though still substantial) during winter conditions (December). 2. Temporal Resilience: The weighted fusion methodology demonstrates remarkable resilience to extended forecasting horizons. While conventional forecasting meth  
Figure 16: Comparisons of time scale accuracy. ods typically show degrading performance as the prediction horizon extends, our approach maintains relatively stable MAPE values even at the 168 h horizon. This suggests that by effectively balancing provincial forecasts based on their demonstrated reliability, our methodology successfully preserves long-term forecasting accuracy. 3. Operational Implications: The superior performance of weighted fusion at very short horizons (1-6 h) has particularly significant operational implications for grid dispatch centers. These ultra-short-term forecasts directly inform real-time operational decisions, making the substantial accuracy improvements (approximately 60-70% error reduction in comparison with direct aggregation) critically valuable for maintaining grid stability and economic operation.