HiFAKES: Synthetic High-Frequency NILM Data for NILM Models Diagnostics and Generalization Testing

Monitoring electricity consumption at the appliance level is crucial for increasing energy efficiency in residential and commercial buildings. Using a single meter, the non-intrusive load monitoring (NILM) breaks down household consumption down to appliance-level, providing comprehensive insights into end-user electricity behavior. NILM models are trained on a household's total power consumption paired with submetered appliance labels. When sampled at high frequencies ($\geq$ 1 kHz), these datasets capture the full waveform characteristics, significantly improving disaggregation accuracy and model generalization. Nevertheless, such datasets are scarce, collected from a limited number of households, and rarely include labels for power estimation, which complicates their use for model training, evaluation, or debugging. We propose HiFAKES, a pre-trained synthetic data generator that can instantly generate unlimited amounts of fully labeled high-frequency NILM data, including aggregated and submetered current signatures. The data is ready-to-use and annotated for load identification (classification) and power estimation (regression). It allows simulating seen and completely unseen scenarios of appliances' behavior with full control over the number of appliance classes, operational modes, class similarity, brand diversity, and the number of concurrently running devices. We propose a structured methodology to test the generalization of NILM models on simulated unseen households. The reliability of the HiFAKES synthetic data is assessed using a domain-agnostic 3-dimensional metric. The generated signatures achieve high realism (93\% authenticity), closely resemble real-world data (84\% fidelity), and include a reasonable portion of unseen signatures (5\%).