Research on Event Detection Algorithm of Non-Intrusive Load Monitoring and Decomposition

Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

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Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

Applied Computational Intelligence and Soft Computing ◽

10.1155/2012/742461 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Lei Jiang ◽

Jiaming Li ◽

Suhuai Luo ◽

Sam West ◽

Glenn Platt

Keyword(s):

Support Vector Machine ◽

Event Detection ◽

Recognition Rate ◽

Detection Algorithm ◽

Classification Method ◽

Support Vector ◽

Signature Analysis ◽

Turn On ◽

Power Load ◽

Load Monitoring

Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.

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A Triple-threshold-based Load Event Detection Algorithm for Non-intrusive Load Monitoring

Journal of Physics Conference Series ◽

10.1088/1742-6596/2065/1/012011 ◽

2021 ◽

Vol 2065 (1) ◽

pp. 012011

Author(s):

Mingming Chen ◽

Kaijie Fang ◽

Qifeng Huang ◽

Shihai Yang ◽

Hanmiao Cheng ◽

...

Keyword(s):

Event Detection ◽

Detection Algorithm ◽

High Threshold ◽

Test Results ◽

Load Fluctuation ◽

Static Data ◽

Load Monitoring ◽

Power Threshold ◽

The Common ◽

Monitoring Algorithm

Abstract Event detection is an important foundation of non-intrusive load monitoring algorithm. In this paper, the common household appliance load events are classified, and a new triple-threshold event detection algorithm is proposed aimed at solving the problems of false detection and missing detection in the practical application. Firstly, a low power threshold is used to realize high-sensitive detection of the load events, and secondly the detected events are spliced according to the time threshold to get the complete events. Thirdly, the high threshold is used to discriminate the complete event set to filter out the disturbance caused by load fluctuation. Finally, the results are modified with a correction logic. The test results carried with static data show that, the algorithm proposed in this paper is more accurate for positioning the time of putting into and cutting off load, which is conducive to improve the accuracy of transient interval interception of load events, and has advantages in detecting slow rising load events. In addition, the algorithm proposed in this paper has a small amount of calculation, which can meet the requirements of application in the hardware of smart meter.

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