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FIRED: A Fully-labeled hIgh-fRequency Electricity Disaggregation Dataset

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Published:18 November 2020Publication History

ABSTRACT

As more and more homes are equipped with smart electricity meters, home owners gain the ability to monitor their total electricity consumption on a daily or hourly basis. Techniques such as load forecasting, load disaggregation, and activity recognition try to provide even better insights into our electricity consumption, highlight saving potential or improve our daily living. To develop and evaluate these techniques, publicly available datasets are used. We identified a lack of high frequency fully labeled electricity datasets in the residential domain and present the FIRED dataset. It contains 52 days of 8 kHz aggregated current and voltage readings of the 3-phase supply of a typical residential apartment in Germany. The dataset also contains synchronized ground truth data as 2 kHz readings of 21 individual appliances, as well as room temperature readings and fully labeled state changes of the lighting system, resulting in a complete and versatile residential electricity dataset.

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      • Published in

        cover image ACM Other conferences
        BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
        November 2020
        361 pages
        ISBN:9781450380614
        DOI:10.1145/3408308

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        Publication History

        • Published: 18 November 2020

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        Acceptance Rates

        BuildSys '20 Paper Acceptance Rate38of139submissions,27%Overall Acceptance Rate148of500submissions,30%

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