Abstract
Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by one smart-meter. In this article we introduce the use of fractional calculus in the Non-Intrusive Load Monitoring task. Specifically the aggregated active power signal is transformed to its fractional derivatives incorporating temporal information properties of the input signal to the Non-Intrusive Load Monitoring architecture. The performance of the proposed methodology was evaluated in two publicly available datasets namely REDD and AMPds2 using Convolutional Neural Networks and Recurrent Neural Networks as regression models. The proposed approach improves the estimation accuracy by 3.4% when compared to the baseline energy disaggregation setup achieving a maximum disaggregation accuracy of 90.8%.
| Original language | English |
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| Title of host publication | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509066315 |
| ISBN (Print) | 9781509066322 |
| DOIs | |
| Publication status | Published - 14 May 2020 |
| Event | 45th International Conference on Acoustics, Speech, and Signal Processing - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020 Conference number: 45 https://2020.ieeeicassp.org/ |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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| Volume | 2020-May |
| ISSN (Print) | 1520-6149 |
Conference
| Conference | 45th International Conference on Acoustics, Speech, and Signal Processing |
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| Country/Territory | Spain |
| City | Barcelona |
| Period | 4/05/20 → 8/05/20 |
| Internet address |
Keywords
- Energy Disaggregation
- Fractional Calculus
- Non-Intrusive Load Monitoring (NILM)