BO-WDMS-BiLSTM: A Bayesian-Optimized Wide-and-Deep Multi-Scale Bidirectional Model for Electricity Theft Detection

As utilities transition from legacy data-collection devices to advanced smart meters, theft detection must remain accurate, scalable, and actionable—helping teams prioritize field inspections, reduce false alarms, and allocate limited enforcement resources more efficiently across service territories. We propose BO-WDMS-BiLSTM, a dual-branch model that combines customer and billing information with electricity-use patterns over time. One branch learns from compact customer indicators and captures stable signals that remain useful even when daily usage changes for normal reasons. The other branch converts multi-week consumption data into a structured representation and extracts patterns at different time scales, enabling it to detect both short abnormal drops and gradual shifts. A bidirectional sequence module then interprets each period using context from both earlier and later days. The two branches are fused with a residual-style head that preserves information from both sources and improves training stability, while Bayesian optimization reduces manual tuning. Compared with widely used classical and deep learning baselines, the proposed model achieved the best overall discrimination and ranked more true theft cases at the top of the inspection list, improving the strongest baseline from 0.7897 to 0.7977 and raising top-100/top-200 inspection performance from 0.9320/0.9204 to 0.9797/0.9394.