Non-Intrusive Load Monitoring System Based on Convolution Neural Network and Adaptive Linear Programming Boosting

Chao Min, Guoquan Wen, Zhaozhong Yang, Xiaogang Li and Binrui Li
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Chao Min: School of Science, Southwest Petroleum University, Chengdu 610500, China
Guoquan Wen: School of Science, Southwest Petroleum University, Chengdu 610500, China
Zhaozhong Yang: State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Xiaogang Li: State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
Binrui Li: School of Science, Southwest Petroleum University, Chengdu 610500, China

Energies, 2019, vol. 12, issue 15, 1-23

Abstract: Non–intrusive load monitoring based on power measurements is a promising topic of appliance identification in the research of smart grid; where the key is to avoid the power sub-item measurement in load monitoring. In this paper; a three–step non–intrusive load monitoring system (TNILM) is proposed. Firstly; a one dimension convolution neural network (CNN) is constructed based on the structure of GoogLeNet with 2D convolution; which can zoom in on the differences in features between the different appliances; and then effectively extract various transient features of appliances. Secondly; comparing with various classifiers; the Linear Programming boosting with adaptive weights and thresholds (ALPBoost) is proposed and applied to recognize single–appliance and multiple–appliance. Thirdly; an update process is adopted to adjust and balance the parameters between the one dimension CNN and ALPBoost on–line. The TNILM is tested on a real–world power consumption dataset; which comprises single or multiple appliances potentially operated simultaneously. The experiment result shows the effectiveness of the proposed method in both identification rates.

Keywords: non–intrusive load monitoring; appliance identification; convolution neural network; adaptive linear programming boosting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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