一种非侵入式低功耗高精度的用电器识别算法

doi:10.19665/j.issn1001-2400.2023.01.017

Abstract

Abstract:

Non-intrusive electrical appliance identification is the key technology to realize customer-side intelligent sensing in the ubiquitous power Internet of Things.Aiming at the problem that the calculation complexity of the existing non-intrusive electrical appliance identification system is too high and is not conducive to industrialization,a low-cost,low-power consumption,high-precision non-intrusive electrical appliance identification system and the algorithm based on the Long Short-Term Memory(LSTM) network are proposed,and they can be applied to an embedded microcontroller.First,the current on the bus is collected synchronously.Second,the proposed multi-parameter detection method is used to judge the switching event of the electrical appliance.Third,the LSTM network is used to process the data before and after the switching time point,and the type of electrical appliance which is switching is obtained.Finally,the current types and quantities of electrical appliances are judged by the cumulative sum.Simulation and measurement results show that only a small amount of data training for a single electric appliance is needed,and that the recognition accuracy of combined electric appliances can be up to 99.6% in the proposed embedded microcontroller system,in which the power consumption is less than 1.5 watts.The proposed algorithm can be further applied to the statistics of the switching time point,service time and total power consumption of each electrical appliance,which provides refined user power consumption information for the smart grid.and provides an important reference for the energy management and optimization in the smart grid.

Key words: internet of things, LSTM, identification, electrical parameter measurement

CLC Number:

TM714

WU Boyun,GU Wenjie,HE Xiandeng. Low-power consumption high-precision non-intrusive electrical appliance identification algorithm[J].Journal of Xidian University, 2023, 50(1): 149-157.

Figures/Tables 12

References 19

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编号	用电器类型	A_cc/%	P_re/%	R_ec/%	F₁
1	冰箱	100.00	100.00	100.00	1.000
2	后院灯	100.00	100.00	100.00	1.000
3	吹风机	100.00	100.00	100.00	1.000
4	浴室顶灯	98.40	98.90	97.80	0.983
5	卧室灯	97.30	94.20	100.00	0.971
6	厨房切碎机	99.80	100.00	95.20	0.976
平均		99.25	98.85	98.83	0.988

编号	用电器类型	ND-LSTM	Adaboost-BP	UIF-NILM	Fisher判别
1	冰箱	100.00	100.00	100.00	71.40
2	后院灯	100.00	100.00	98.10	100.00
3	吹风机	100.00	100.00	100.00	100.00
4	浴室顶灯	98.40	96.90	96.10	71.90
5	卧室灯	97.30	83.30	100.00	83.30
6	厨房切碎机	99.80	80.00	100.00	100.00
平均识别率		99.25	93.37	99.03	87.77

编号	用电器名称	cos(?(1))	D(3)	D(5)	P_max/W	P_av/W	I/mA	V_t
1	1号自制用电器	0.20	0.31	0.14	1.46	0.98	43	0.55
2	LED灯	1.00	0.15	0.09	1.48	0.99	45	1.14
3	2号自制用电器	0.80	0.28	0.15	1.45	0.97	42	0.93
4	电风扇	0.50	0.20	0.12	1.49	0.99	202	2.26
5	47 kΩ电阻	1.00	0.00	0.00	1.48	0.98	5	1.00
6	电吹风	0.90	0.08	0.01	1.45	0.96	8 550	89.68
7	节能灯	0.27	0.30	0.08	1.47	0.97	49	0.71
8	电源	-0.30	0.40	0.10	1.48	0.99	18	8.92

编号	用电器名称	识别准确度
编号	用电器名称	ND-LSTM	UIF-NILM
1	1号自制用电器	99.4	96.2
2	LED灯	99.7	96.7
3	2号自制用电器	98.9	98.1
4	电风扇	99.8	99.2
5	47 kΩ电阻	99.9	90.5
6	电吹风	100.0	96.2
7	节能灯	99.7	97.3
8	电源	99.4	98.1
	平均	99.6	96.5

编号	用电器名称	系统功耗
编号	用电器名称	ND-LSTM	UIF-NILM
1	1号自制用电器	1.47	5.9
2	LED灯	1.47	5.8
3	2号自制用电器	1.46	6.1
4	电风扇	1.49	6.2
5	47 kΩ电阻	1.48	5.9
6	电吹风	1.46	6.0
7	节能灯	1.47	6.1
8	电源	1.46	5.8
	平均	1.47	5.97

Low-power consumption high-precision non-intrusive electrical appliance identification algorithm

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