Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model

Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models...

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Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 21; p. 5629
Main Authors Peng, Ce, Lin, Guoying, Zhai, Shaopeng, Ding, Yi, He, Guangyu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2020
Subjects
Algorithms
Appliances
deep appliance group model
Deep learning
deep user model
Efficiency
Electric rates
Energy consumption
Energy usage
Machine learning
Monitoring
NILM
Normal distribution
Representations
Time series
Use statistics
user behavior
Wavelet transforms
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Summary:Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13215629