Abstract
Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model.
Highlights
It is well known that due to growing energy demands, in the last few decades, energy conservation is becoming challenging
Feature Fused Appliance Recognition Model (FF-AR) In [10] we propose a discriminative AR model based on Support Vector Machines (SVM), whereas the selection of optimal feature set and comparison with other machine learning models have been presented
This impacts the overall classification accuracy of the FHMM-AR models, as it can be seen from the results reported for test cases 5 to 10
Summary
It is well known that due to growing energy demands, in the last few decades, energy conservation is becoming challenging. High-power appliances exhibit distinct steady-state signatures, devices with low-power consumption profile are difficult to disaggregate/recognize from the aggregated load measurements due to overlapping steady-state features. We have considered binary as well as multi-state operation of the devices in our experimental evaluations, along with adequate feature set selection for optimal classification.
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