A Novel Instrumentation Approach for Clustered Appliance Load Monitoring

Dash, Shitikantha; Sodhi, Ranjana; Sodhi, Balwinder

doi:10.1109/pesgm41954.2020.9281645

Cited by 1 publication

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“…Taking into account the limitations presented by the electrical network topology, main challenges of SILM are loosely described as obtaining a minimal number of meters and proper partitioning of appliances which make possible extracting the consumption profiles of individual appliances with high accuracy [11]. Besides, the types and number of appliances, the infrastructure of residential buildings that imposes constraints and shrinks the set of feasible subgroups of appliances [22], and the willingness of consumers to pay for the meters are not the same across the board and must be taken into consideration [18]. Therefore, the proper number of meters should be determined individually for each consumer.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Disaggregation Difficulty with Respect to the Number of Meters

Azizi,

Beheshti,

Bolouki

2021

Preprint

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A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption profiles of appliances within a residence by analyzing the aggregated consumption signal. Among efficient NILM methods are event-based algorithms in which events of the aggregated signal are detected and classified in accordance with the appliances causing them. The large number of appliances and the presence of appliances with close consumption values are known to limit the performance of event-based NILM methods. To tackle these challenges, one could enhance the feature space which in turn results in extra hardware costs, installation complexity, and concerns regarding the consumer's comfort and privacy. This has led to the emergence of an alternative approach, namely semi-intrusive load monitoring (SILM), where appliances are partitioned into blocks and the consumption of each block is monitored via separate power meters. While a greater number of meters can result in more accurate disaggregation, it increases the monetary cost of load monitoring, indicating a trade-off that represents an important gap in this field. In this paper, we take a comprehensive approach to close this gap by establishing a so-called notion of "disaggregation difficulty metric (DDM)," which quantifies how difficult it is to monitor the events of any given group of appliances based on both their power values and the consumer's usage behavior. Thus, DDM in essence quantifies how much is expected to be gained in terms of disaggregation accuracy of a generic event-based algorithm by installing meters on the blocks of any partition of the appliances. Experimental results based on the REDD dataset illustrate the practicality of the proposed approach in addressing the aforementioned trade-off.

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Section: Introductionmentioning

confidence: 99%