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A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

1
School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China
2
The School of Computer Software, Tianjin University, Tianjin 300354, China
3
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
4
Department of Automotive Engineering, Hebei Institute of Machinery and Electricity, Xintai 054000, China
5
The School of Computer Software, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(2), 51; https://doi.org/10.3390/fi11020051
Received: 11 December 2018 / Revised: 17 January 2019 / Accepted: 21 January 2019 / Published: 19 February 2019
(This article belongs to the Section Smart System infrastructures and Cybersecurity)
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Abstract

In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments. View Full-Text
Keywords: non-intrusive load monitoring; power consumption pattern; load disaggregation; low sampling data; optimized support vector regression non-intrusive load monitoring; power consumption pattern; load disaggregation; low sampling data; optimized support vector regression
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Yuan, Q.; Wang, H.; Wu, B.; Song, Y.; Wang, H. A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data. Future Internet 2019, 11, 51.

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