Participant-level anomaly detection for generation and load data using dual-side LSTMs.

Reliable anomaly detection for generation and load metering data is essential to market settlement. In practice, anomalies are diverse and sparse, and the load side contains a large and time-varying number of participants, which makes fine-grained participant-level localization difficult. Conventional statistical thresholding and generic outlier detectors (e.g., LOF) are often sensitive to nonstationarity and cannot effectively exploit temporal dependency across intra-day time slots, resulting in coarse alarms and high false positives. To address these issues, this paper proposes a dual-side LSTM-based participant-level anomaly detection method. Multimodal features are constructed from 24 intra-day measurements, a daily total, FFT-derived frequency components, and calendar context. A zero-padding and masking mechanism is introduced to handle daily changes in the number of load participants without contaminating model training. A dual-layer LSTM with a 16-d participant embedding learns participant-specific temporal patterns, and a rule combining relative-error screening with confidence verification produces participant-level anomaly positioning and abnormal time-slot identification. Experiments on one-month desensitized provincial-grid data (242 generators and 78k-85k loads) achieve 100.0% F1-score (100.0% recall, 100.0% precision), with highly accurate positioning and time-slot recognition, substantially outperforming statistical and LOF baselines.