# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 1 Background and Motivation
### Literature Review and Research Gaps  
Current short-term load forecasting approaches can be categorized into three main groups: statistical methods, data analysis techniques, and machine learning algorithms. While extensive research has advanced single-region forecasting capabilities, significant gaps remain in multi-regional integration methodologies, particularly for grid-level applications [8; 9]. Statistical approaches represent the foundation of traditional load forecasting methods, valued for their mathematical interpretability and established performance records [10]. These methods primarily focus on extracting temporal trends from historical data to generate forecasts [11]. However, as power systems grow more complex with increasing renewable integration, these traditional statistical approaches face limitations in modeling highly nonlinear relationships and accommodating multiple influential factors simultaneously [12]. Data analysis methods focus on transforming input load sequences to enhance feature extraction and improve prediction accuracy. [PERSON] et al. [13] applied wavelet analysis to decompose load series into multiple frequency bands, modeling each component separately before reconstruction, which significantly improved forecast accuracy during transitional periods. Similarly, [PERSON] et al. [14] utilized complementary ensemble empirical mode decomposition to extract frequency features from similar-day data, effectively capturing both long-term trends and short-term fluctuations. More recent approaches have employed variational mode decomposition to reduce load sequence volatility and facilitate feature extraction [15]. [PERSON] et al. [16] integrated this technique with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to forecast agricultural greenhouse load consumption with improved accuracy. [PERSON] et al. [17] developed a comprehensive clustering method for characterizing user load profiles and assessing their regulation potential in demand response applications. These data analysis approaches substantiate that appropriate preprocessing and feature enhancement can significantly improve forecasting performance, particularly when dealing with complex multi-source data typical in grid-level applications [18]. However, most existing studies focus on single-region applications, with limited exploration of how these techniques can be adapted for multi-regional integration challenges. Machine learning algorithms have revolutionized load forecasting by offering powerful tools for modeling complex nonlinear relationships without requiring explicit mathematical formulations [19]. These approaches have evolved from traditional machine learning methods to sophisticated deep learning architectures specifically designed for temporal sequence modeling. Neural network approaches, particularly deep learning variants, have shown remarkable progress in recent years. Recurrent neural networks (RNNs) were among the first deep architectures applied to time-series forecasting [20]. Later, long short-term memory (LSTM) networks emerged as a superior alternative by addressing the vanishing gradient problem inherent in standard RNNs [21]. [PERSON] et al. [22] combined LSTM with extreme gradient boosting (XGBoost) to create a hybrid prediction model that achieved higher accuracy than either method alone. The Transformer architecture, originally developed for natural language processing by [PERSON] et al. [23], has recently been adapted for time-series forecasting with notable success. Transformers employ self-attention mechanisms to evaluate sequence features, enabling them to better capture long-range dependencies in temporal data [24]. [PERSON] et al. [25] utilized a self-attention encoder combined with deep neural networks for short-term net load forecasting, demonstrating excellence in both deterministic and interval predictions. Recent innovations include the integration of Transformers with other architectures to enhance performance. [PERSON] et al.