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In this paper, the demonstration application project of non-intrusive load monitoring (NILM) technology is introduced, including the NILM-based smart meter, low voltage residential power information acquisition system of Jiangsu Province, data acquisition and display, etc. On this basis, considering the practical conditions, four technical characteristics of NILM system are expounded, which are processing data in different devices, pushing information with different time delays, interacting with users, improving performance continuously. A NILM research platform called cloud-terminal coordination system is built, and an unsupervised learning algorithm is explored, which provides a way of performance evolution for the system. This paper is expected to provide reference for researchers who committee to promoting the engineering application of NILM technology.

Non-intrusive load monitoring (NILM) is an intelligent perception technology of electricity consumption information on the consumer side. Event detection, the fundamental and indispensable step of the NILM framework, has a direct impact on the accuracy of the results of load decomposition. This paper presents a novel NILM event detection approach based on Kalman filter and improved cumulative sum (CUSUM) algorithm. Firstly, this paper use Kalman filter to handle acquired data for sliding windows to make the power curve more smooth and suitable for next event detection step. An adaptive factor is introduced in the traditional CUSUM algorithm to improve the detection accuracy. The experiment results show that the proposed approach can not only effectively detect various input and cutoff events with different power level but also suits for the detection of the long transient events.

Focusing on the current impulse waveforms generated when the fixed or variable frequency air conditioners, vacuum cleaner, microwave oven and other electrical appliances are turned on, the characteristic parameters such as impulse amplitude, rising time, dropping amplitude, and falling time are defined, and a multi-feature Bayesian classification model is established. Considering that the classification algorithm works on the smart meter hardware, the computing and storage resources are limited, as a result that the model scale needs to be reduced as much as possible, a meant parameters Bayesian classification method is proposed. The current impulse waveform samples of different appliances are divided into several groups, and the mean value of the characteristic values of the samples is used as the parameters of the classification model. Under the same model capacity, the generalization error of the model is reduced. The simulation results show that the accuracy of Bayesian classifier constructed with meant value parameters is better than that of ordinary Bayesian classifier. In the laboratory, the impulse classification test is carried out on a single-phase smart meter, and the test results verify the feasibility of the proposed method.