# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 1 Background and Motivation
### Literature Review and Research Gaps
[26] proposed a Transformer-CNN fusion model that combines CNN's multi-feature fusion capabilities with Transformer's temporal modeling strengths, significantly improving prediction accuracy and multi-trend sequence feature extraction. [PERSON] et al. [27] combined Transformers with spatiotemporal graph convolutional networks for short-term load forecasting in integrated energy systems, achieving high prediction accuracy and stability. Data fusion approaches are especially relevant for grid-level forecasting, as they provide systematic frameworks for integrating heterogeneous information from multiple sources [28]. Probabilistic methods like Monte Carlo simulation have been widely used for analyzing uncertainty in power systems. [PERSON] et al. [29] employed Monte Carlo techniques to simulate traveler behavior and assess electric vehicle charging requirements. Artificial intelligence-based fusion approaches have gained prominence for their ability to discover complex relationships in heterogeneous data. [PERSON] et al. [30] developed a multi-source data-and-model fusion method for ultra-short-term bus load forecasting that extractsfeature vectors from numerical and non-numerical data using backpropagation artificial neural networks, combining these with image vectors from CNNs for improved prediction. Some researchers have adopted modular approaches that first complete target perception, then perform association fusion, and finally generate comprehensive decision results [31]. [PERSON] et al. [32] proposed an epidemic-informed load forecasting model based on extreme gradient boosting that incorporates social impact factors, demonstrating short training times and low deviation rates during the COVID-19 pandemic. At the output level, fusion methods can combine multiple forecast results to improve overall accuracy. [PERSON] et al. [33] introduced a fine-grained data fusion approach for load forecasting that generates hourly predictions using multivariate linear regression, derives load scenarios based on independent variable uncertainties, and aggregates these from bottom to top to produce monthly forecasts. [PERSON] et al. [34] investigated the effects of heavy traffic on steel slag asphalt mixtures, finding that high-temperature stability and skid resistance performance could be optimized with specific steel slag content ratios, providing insights into how similar optimization principles might be applied to multi-regional load forecasting integration. Research Gaps in Grid-Level Forecasting Integration  
Despite extensive research in various forecasting methodologies, several critical gaps remain in grid-level load forecast integration. Most research focuses on single-region forecasting techniques, with limited exploration of multi-source data integration strategies essential for grid-level applications [35]. The unique challenges of combining forecasts from regions with heterogeneous characteristics remain insufficiently addressed. Current multi-regional approaches typically employ simple accumulation methods that fail to address data heterogeneity and quality variations across provinces, treating all regional forecasts equally without considering their relative reliability. Few studies have developed systematic evaluation frameworks for quantifying forecast reliability across regions with varying characteristics, making it difficult to objectively assess the trustworthiness of individual forecasts. Existing research lacks comprehensive weighting methodologies that dynamically adjust the importance of regional forecasts based on quantifiable metrics and adapt to changing conditions [36]. These gaps highlight the need for a more sophisticated approach to grid-level forecast integration that systematically evaluates and dynamically combines regional forecasts. Our proposed weighted fusion methodology addresses these challenges by introducing a comprehensive evaluation framework and PCA-based weighting mechanism specifically designed for grid-level applications.