Chao Pang

Accurate extraction of typical load curves (TLCs) is essential for electricity market trading, demand-side management, and optimal design of energy storage systems. However, conventional methods are highly sensitive to anomalous consumption days caused by equipment failures or maintenance, which can distort normal electricity consumption patterns. To address this issue, this paper proposes a two-stage unsupervised framework that integrates a deep sequence model with an anomaly detection algorithm for robust TLC extraction. First, a Transformer-based autoencoder is employed to learn complex temporal dependencies and intrinsic patterns from historical daily load data, extracting robust periodic features by reconstructing the input load sequences. Subsequently, the reconstruction error of each daily load curve is computed as an anomaly assessment metric. These reconstruction error features are then fed into an Isolation Forest algorithm to identify anomaly loads that significantly deviate from the learned normal patterns, without requiring predefined thresholds or labeled data. Validation using real-world commercial and industrial electricity consumption data demonstrates that the proposed method effectively filters out various anomalies (e.g., spikes, troughs, and shape distortions) that conventional methods fail to exclude. The extracted TLCs exhibit improved robustness and representativeness. Further case studies indicate that adopting purified TLCs to guide electricity procurement in market trading facilitates more scientific trading strategies and avoids increased electricity costs caused by distorted load patterns. In summary, the proposed Transformer-Isolation Forest hybrid framework provides an effective data-driven solution for robust TLC extraction. The resulting TLCs can be directly used to guide day-ahead market bidding, optimize power purchase contract decomposition, and assess user demand response potential.