Appliance Classification by Power Signal Analysis Based on Multi-Feature Combination Multi-Layer LSTM

The imbalance of power supply and demand is an important problem to solve in power industry and Non Intrusive Load Monitoring (NILM) is one of the representative technologies for power demand management. The most critical factor to the NILM is the performance of the classifier among the last steps o...

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Bibliographic Details
Published inEnergies (Basel) Vol. 12; no. 14; p. 2804
Main Authors Kim, Jin-Gyeom, Lee, Bowon
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 21.07.2019
Subjects
Accuracy
Algorithms
appliance classification
Appliances
Classification
Datasets
deep learning
Electric industries
feature extraction
Information processing
Long short-term memory
Machine learning
Monolayers
multi-feature combination
Multilayers
power signal
Power supply
recurrent neural network
Signal analysis
Signal classification
Signal processing
Support vector machines
time-series
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Summary:The imbalance of power supply and demand is an important problem to solve in power industry and Non Intrusive Load Monitoring (NILM) is one of the representative technologies for power demand management. The most critical factor to the NILM is the performance of the classifier among the last steps of the overall NILM operation, and therefore improving the performance of the NILM classifier is an important issue. This paper proposes a new architecture based on the RNN to overcome the limitations of existing classification algorithms and to improve the performance of the NILM classifier. The proposed model, called Multi-Feature Combination Multi-Layer Long Short-Term Memory (MFC-ML-LSTM), adapts various feature extraction techniques that are commonly used for audio signal processing to power signals. It uses Multi-Feature Combination (MFC) for generating the modified input data for improving the classification performance and adopts Multi-Layer LSTM (ML-LSTM) network as the classification model for further improvements. Experimental results show that the proposed method achieves the accuracy and the F1-score for appliance classification with the ranges of 95–100% and 84–100% that are superior to the existing methods based on the Gated Recurrent Unit (GRU) or a single-layer LSTM.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12142804