Multi-Label Deep Blind Compressed Sensing for Low-Frequency Non-Intrusive Load Monitoring
Shikha Singh, Angshul Majumdar
Abstract
Current non-intrusive load monitoring (NILM) algorithms are reasonably accurate when the sampling rate is high. However, in practical scenarios, when the sampling frequency is low, the performance of most algorithms deteriorates. This work proposes a solution for low-frequency NILM. We propose to modify the smart-meter such that it can transmit at low frequency using principles of compressed sensing (CS). From such CS samples, we propose to detect the state of the appliance by using a multi-label consistent version of deep blind compressed sensing. Comparison with existing techniques shows that our proposed approach yields considerably better results.
Topics & Concepts
Compressed sensingComputer scienceSampling (signal processing)Smart meterReal-time computingState (computer science)Low frequencyTime–frequency analysisElectronic engineeringArtificial intelligenceEngineeringAlgorithmSmart gridComputer visionTelecommunicationsElectrical engineeringDetectorFilter (signal processing)Smart Grid Energy ManagementSparse and Compressive Sensing TechniquesPower Line Communications and Noise