Acoustic based appliance state identifications for fine-grained energy analytics

Abstract

Fine-grained monitoring of everyday appliances can provide better feedback to the consumers and motivate them to change behavior in order to reduce their energy usage. It also helps to detect abnormal power consumption events, long-term appliance malfunctions and potential safety concerns. Commercially available plug meters can be used for individual appliance monitoring but for an entire house, each such individual plug meters are expensive and tedious to setup. Alternative methods relying on Non-Intrusive Load Monitoring techniques help disaggregate electricity consumption data and learn about the individual appliance's power states and signatures. However fine-grained events (e.g., appliance malfunctions, abnormal power consumption, etc.) remain undetected and thus inferred contexts (such as safety hazards etc.) become invisible. In this work, we correlate an appliance's inherent acoustic noise with its energy consumption pattern individually and in presence of multiple appliances. We initially investigate classification techniques to establish the relationship between appliance power and acoustic states for efficient energy disaggregation and abnormal events detection. While promising, this approach fails when there are multiple appliances simultaneously in 'ON' state. To further improve the accuracy of our energy disaggregation algorithm, we propose a probabilistic graphical model, based on a variation of Factorial Hidden Markov Model (FHMM) for multiple appliances energy disaggregation. We combine our probabilistic model with the appliances acoustic analytics and postulate a hybrid model for energy disaggregation. Our approach helps to improve the performance of energy disaggregation algorithms and provide critical insights on appliance longevity, abnormal power consumption, consumer behavior and their everyday lifestyle activities. We evaluate the performance of our proposed algorithms on real data traces and show that the fusion of acoustic and power signatures can successfully detect a number of appliances with 95% accuracy.

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

“…A suitable broadband antenna sensor and data acquisition system allows this disturbance to be recorded and characterized [42]. Similarly, in Reference [43], the acoustic noise produced during operation was used to identify the type of device.…”

Fine-grained appliance usage and energy monitoring through mobile and power-line sensing

“…In [53], EMI noise is used to detect the operating states of the appliances, for example, the surface on which vacuum cleaner is being used to improve energy disaggregation. Acoustic noise of appliances have been captured to identify the micro-states of the appliances which help in fine-grained appliance energy enumeration [54].…”

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

“…An active learning based scalable sleep monitoring framework has been proposed in Hossain et al (2015). A factorial hidden Markov based model has been proposed for acoustic based appliance state identifications for fine grained energy analytics in building environment (Pathak et al 2015; Khan et al 2015c). …”