- Title
- Parallel LSTM Architectures for Non-Intrusive Load Monitoring in Smart Homes
- Creator
- Mobasher-Kashani, Mohammad; Noman, Nasimul; Chalup, Stephan
- Relation
- 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020. Proceedings of 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (Canberra, ACT 01-04 December, 2020) p. 1272-1279
- Publisher Link
- http://dx.doi.org/10.1109/SSCI47803.2020.9308592
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- Non-Intrusive Load Monitoring (NILM) is becoming popular as an appliance monitoring technique that can extract detailed power consumption information related to the different appliances used in a household. NILM utilises time series analysis methods to disaggregate signals of operating appliances from a single point in houses. Based on that it can help to provide energy consumption advice for household owners. NILM is known to be a challenging task from a computational aspect. This study proposes deep neural network models with a Parallel Long Short-Term Memory Topology (PLT) and evaluates them on the public REDD dataset. The new models' experimental evaluation results exhibit a significant improvement in four main state-based and energy-based metrics in comparison to previous results obtained using deep denoising autoencoders.
- Subject
- load monitoring; parallel architectures; electric load management; smart homes; SDG 13; SDG 15; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1443446
- Identifier
- uon:41997
- Identifier
- ISBN:9781728125473
- Language
- eng
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