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On the Relationship between Seasons of the Year and Disaggregation Performance

Published: 18 November 2020 Publication History

Abstract

This paper pursues the question of how seasons of the year affect disaggregation performance in Non-Intrusive Load Monitoring. To this end, we select the dishwasher, a common household appliance that may exhibit usage cycles depending on the user. We utilize an auto-correlation function to detect usage patterns of dishwashers in each season. Then, we examine the dissimilarity across each season with the help of the Keogh Lower Bound measure. Finally, we conduct a disaggregation study using the REFIT dataset and relate the outcome to the dissimilarity across seasons. Our findings indicate that in cases where energy consumption shows similarity throughout seasons, the performance of load disaggregation approaches can be positively affected.

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Cited By

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  • (2022)A novel methodology for identifying appliance usage patterns in buildings based on auto-correlation and probability distribution analysisEnergy and Buildings10.1016/j.enbuild.2021.111618256(111618)Online publication date: Feb-2022

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        cover image ACM Other conferences
        NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring
        November 2020
        109 pages
        ISBN:9781450381918
        DOI:10.1145/3427771
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Publication History

        Published: 18 November 2020

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        Author Tags

        1. Auto-Correlation
        2. NILM
        3. Performance
        4. Seasonality
        5. Similarity

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        • Fundação para a Ciência e a Tecnologia

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        • (2022)A novel methodology for identifying appliance usage patterns in buildings based on auto-correlation and probability distribution analysisEnergy and Buildings10.1016/j.enbuild.2021.111618256(111618)Online publication date: Feb-2022

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