A low complexity binary-weighted energy disaggregation framework for residential electricity consumption

The discipline of Non-Intrusive Load Monitoring (NILM) has witnessed a surge in the application of machine learning and pattern recognition approaches, enabling researchers to investigate NILM problems. This paper introduces a novel energy disaggregation system that employs a binary-weight matrix to separate the power consumption into distinct signal patterns. The framework includes filtering techniques, followed by event detection and energy disaggregation. To address the challenges of event detection, a regional threshold-based algorithm is developed, eliminating the need for predefined thresholds. A comprehensive complexity analysis of the developed algorithms reveals a reduced computational complexity, making the framework suitable for real-time deployment. For performance assessment, the Reference Energy Disaggregation dataset (REDD) and Energy Monitoring via Building Electricity Disaggregation dataset (EMBED) are utilized. A frequency of 1 Hz is maintained to ensure accurate evaluation. The proposed event detection algorithm achieves a precision of 92.5% and an f1-score of 73.6% on EMBED data, improving average precision by 23.5% and a substantial reduction in false positives compared to an existing method. The energy disaggregation algorithm separates the power consumption of four devices in 15.6 s and the entire framework (filtering, event detection, and energy disaggregation) takes 15.9 s for execution, all achieved using a semi-supervised training-less approach.