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DC Field | Value | Language |
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dc.contributor.author | Dash, S. | - |
dc.contributor.author | Gandhi, K. | - |
dc.contributor.author | Sodhi, R. | - |
dc.date.accessioned | 2021-08-12T23:03:06Z | - |
dc.date.available | 2021-08-12T23:03:06Z | - |
dc.date.issued | 2021-08-13 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/2387 | - |
dc.description.abstract | Knowing the power consumption of individual household appliances is useful for end-user as well as utilities. There are two ways for appliance load monitoring (ALM), namely intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). This paper focuses on the NILM approach, and discusses a simple yet effective method to improve its accuracy by constructing a better knowledge-base. The proposed methodology is initially verified with the simulation using the Reference Energy Disaggregation Data (REDD) dataset, and later tested on a lab-scale hardware setup as well. Test results reveal that careful construction of knowledge-base can increase the performance of NILM algorithms. MATLAB is used as the programming platform | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Automatic State Detection | en_US |
dc.subject | Non-intrusive Load Monitoring | en_US |
dc.subject | Non-intrusive Load Monitoring | en_US |
dc.title | An automatic state detection algorithm for Non-intrusive load monitoring | en_US |
dc.type | Article | en_US |
Appears in Collections: | Year-2019 |
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Full Text.pdf | 767.48 kB | Adobe PDF | View/Open Request a copy |
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