The oncoming modernization process of the power grids, driven above all by decarbonisation objectives and the continuous improvement of digital technologies, is encouraging active participation in the electricity market by consumers through the Demand-Response mechanism. From this perspective, the introduction of smart meters and energy consumption monitoring devices plays a fundamental role, being able to give benefits to consumers, suppliers and the electricity grid itself. This paper proposes a supervised method of non-intrusive load monitoring (NILM) based on the recognition of patterns in the time domain with the Dynamic Time Warping algorithm which is suitable for low-cost smart metering applications in dwelling. The technique has been tested with the Natural Dataset of the LIT-Dataset showing a recognition success rate of 100%.
Fontana, C., Riva Sanseverino, E. (2021). On the Non-Intrusive Load Monitoring in dwellings: a feasibility perspective. In 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (pp. 1-5). Bari [10.1109/EEEIC/ICPSEurope51590.2021.9584518].
On the Non-Intrusive Load Monitoring in dwellings: a feasibility perspective
Fontana, Claudio
Writing – Original Draft Preparation
;Riva Sanseverino, Eleonora
2021-09-01
Abstract
The oncoming modernization process of the power grids, driven above all by decarbonisation objectives and the continuous improvement of digital technologies, is encouraging active participation in the electricity market by consumers through the Demand-Response mechanism. From this perspective, the introduction of smart meters and energy consumption monitoring devices plays a fundamental role, being able to give benefits to consumers, suppliers and the electricity grid itself. This paper proposes a supervised method of non-intrusive load monitoring (NILM) based on the recognition of patterns in the time domain with the Dynamic Time Warping algorithm which is suitable for low-cost smart metering applications in dwelling. The technique has been tested with the Natural Dataset of the LIT-Dataset showing a recognition success rate of 100%.File | Dimensione | Formato | |
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