Open AccessA Simple Method of Non-Intrusive Load Monitoring Based on BP Neural Network
Author(s) -
Luhao Zhang,
Hui Zhu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2237/1/012010
Subject(s) - computer science , artificial neural network , event (particle physics) , energy (signal processing) , task (project management) , data mining , feature extraction , artificial intelligence , energy consumption , feature (linguistics) , simple (philosophy) , differential (mechanical device) , pattern recognition (psychology) , machine learning , engineering , statistics , mathematics , linguistics , physics , philosophy , electrical engineering , systems engineering , epistemology , quantum mechanics , aerospace engineering
Non-Intrusive Load Monitoring (NILM) is a method to breakdown the total power consumption into individual application, and it has great value for improving energy utilization. Deep-learning methods on high frequency data usually have a complex structure. Here, we use BP network with single hidden layer to achieve the task of NILM and get great results. The methods in this paper include event detection, feature extraction to get event-based differential current, data augmentation and application classification. BLUED dataset is used for this experiment. Our method has a good performance in BLUED dataset, with above 95% accuracy on phase A and B for application classification.