Low Resolution Feature Evaluation and Appliance Recognition
Date Issued
2011
Date
2011
Author(s)
Wang, Yu-Chun
Abstract
Appliance state recognition method distinguishes the status of each appliance through smart meters, reduces energy consumption by providing residents with the energy information. However, most researches extract features without evaluating, and may not perform the best efficiency of their algorithm. On the other hand, high cost sensors and the difficulty in deployment not only frustrate the residents, but also decrease the user usability.
In this paper, I evaluate features of appliance power consumption with 4 evaluation functions (Euclidean distance measure、Fuzzy Entropy、Max-Relevance and mRMR), find out the best low resolution feature for appliance state recognition method. To reduce the cost, I use low resolution feature data as input of non-intrusive load monitoring (NILM) system. Provide appliances combination data predict method, avoid exhaustive training and decrease the training effort on the user. To improve accuracy, adjust weight parameters in the algorithm by comparing with last result.
The experimental results show that variance of current in frequency domain performs best when using single feature. For multi-dimension feature, the subset composed of variance of current in frequency domain, minimum variance ratio of inactive power in time domain, average of power factor in frequency domain and average of apparent power in frequency domain has the highest score in feature evaluating. In appliance state recognition, the algorithm provided in this paper reached about 80% joint accuracy in 2 dataset, using average of active power and average of apparent power as the subset of features.
Subjects
Appliance Recognition
Feature Selection
Non-Intrusive Load Monitoring
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-100-R98525037-1.pdf
Size
23.54 KB
Format
Adobe PDF
Checksum
(MD5):99f7810364c048e9495a8f0dfd4ab567