Load Disaggregation Using Microscopic Power Features and Pattern Recognition

A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing...

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Bibliographic Details
Published in:Energies (Basel) 2019, Vol.12 (14), p.2641
Main Authors: de Souza, Wesley Angelino, Garcia, Fernando Deluno, Marafão, Fernando Pinhabel, da Silva, Luiz Carlos Pereira, Simões, Marcelo Godoy
Format: Article
Language:English
Subjects:
Accuracy
Air conditioning
Algorithms
Appliances
Artificial intelligence
Automatic meter reading
Classification
cognitive meters
Computer engineering
Computer simulation
Consumers
Datasets
Disaggregation
Efficiency
Electric appliances
Electric power generation
Electricity
Electricity consumption
Embedded systems
Energy conservation
Energy consumption
Energy efficiency
Energy management
Energy management systems
Feasibility studies
Feature recognition
Household appliances
Infrastructure
Learning algorithms
load disaggregation
Machine learning
NILM
Pattern recognition
Power plants
state machine
State machines
Ubiquitous computing
Virtual power plants
Waveforms
Wavelet transforms
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Summary:A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12142641