A Method for Statistical Estimation of Medium and Long-term Energy Consumption in Non-Intrusive Electric Load Monitoring

Abstract

A statistical estimation method for medium and long-term energy consumption of non-intrusive electric load monitoring of the present invention uses a Gaussian mixture model for equipment identification of non-intrusive electric load monitoring. It is indeed possible to obtain more accurate energy consumption estimates through the accumulation of single-event confidence, so as to solve the problems of electrical appliance feature overlap, measurement, and noise in the use of electrical appliances. The distribution of electricity consumption can significantly improve the accuracy of long-term energy consumption estimation results in non-intrusive load monitoring algorithms. In the case of limited types of electrical appliances, the accuracy rate is above 80%. In terms of hardware, edge computing is adopted to reduce the pressure of cloud data processing. Non-intrusive load monitoring and probability-based mid-to-long-term energy consumption estimation are conducive to the optimization of user-side energy consumption structure.

Description

technical field [0001] The invention belongs to the technical field of smart grid and big data analysis, and in particular relates to a method for estimating medium and long-term energy consumption of non-intrusive load monitoring in an actual power consumption environment. Background technique [0002] Non-intrusive load monitoring (NILM) refers to a new detailed monitoring and analysis technology of electric load, that is, without large-scale arrangement of measurement points at the end of electric load, the algorithm can be used Based on the measurement of bus power consumption data, identify the energy consumption of different electrical appliances connected under the bus, so that users can obtain more specific power consumption data, and use this as a basis to achieve user demand side management, energy structure optimization, etc. It is of great significance to save energy and reduce costs. [0003] Compared with the traditional intrusive power load monitoring technol...

AI Technical Summary

Problems solved by technology

Using this scheme, although it is impossible to provide users with accurate identification results of a single power consumption event, in terms of statistics on the medium and long-term power consumption of electrical appliances, more accurate information can be obtained by accumulating the confidence of a single event. Energy consumption estimation, so as to solve the problems of electrical appliance feature overlap, measurement and use noise, etc., thereby significantly improving the accuracy of long-term estimation results in non-intrusive load monitoring algorithms

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