Abstract
Non-Intrusive Load Monitoring is the decomposition of each appliance energy from the total energy recorded at a central smart meter. The success of machine learning based disaggregation models immensely depends on the quality of the input data. This paper evaluates different normalization techniques and their impact on the performance of a Bi-directional Long Short-Term Memory (BiLSTM) model for energy disaggregation. The group of appliances consists of a non-linear device (television), three multi-sate devices (washing machine, rice cooker and microwave) and two continuously operating devices (refrigerator and Kimchi refrigerator). The comparison was conducted on two publicly available datasets and the results showed that applying normalization to the input samples rather than feature values can improve the performance significantly. The best results have been obtained for normalization with L2-norm.
Original language | English |
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Title of host publication | 2020 International Conference on Data Analytics for Business and Industry |
Subtitle of host publication | Way Towards a Sustainable Economy, ICDABI 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728196756 |
DOIs | |
State | Published - 26 Oct 2020 |
Publication series
Name | 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 |
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Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Energy disaggregation
- data preprocessing
- deep learning
- load monitoring
- machine learning
- multi-target disaggregation
- simultaneous disaggregation
ASJC Scopus subject areas
- Business, Management and Accounting (miscellaneous)
- Computer Networks and Communications
- Information Systems and Management
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering