Non-invasive load identification algorithm based on hybrid neural network and ensemble learning

Abstract

The invention belongs to the data mining and machine learning field and relates to a non-invasive load identification algorithm based on a hybrid neural network and ensemble learning. According to the method, experimental data are processed, so that the format of the data conforms to the input formats of models; after the data are processed, a hybrid neural network model is established; the data are input into the model; the model is trained and tested, identification results are obtained; and voting is performed for the results of three different models based on the idea of ensemble learning, so that a final identification result is obtained. With the method adopted, the feature extraction effect and load identification effect of the hybrid neural network are better than the effects of a traditional neural network; an ensemble learning idea-based method is provided, a plurality of feature subsets are selected from a total feature set so as to train a plurality of base classifiers, and the base classifiers are combined, and therefore, variance can be decreased, and the identification effect of the final identification result can be improved, and the problem of adverse influence of the introduction of harmonic features on an identification effect can be solved.

Description

technical field [0001] The invention belongs to the field of data mining and machine learning, and in particular relates to a non-invasive load identification algorithm based on mixed neural network and integrated learning. Background technique [0002] There are two methods of electrical load monitoring: one is intrusive load monitoring (ILM) and the other is non-intrusive load monitoring (NILM). Traditional intrusive load monitoring needs to install a monitoring device for each electrical appliance in the home to obtain the data of the electrical appliance, and then transmit the acquired data to the terminal through the network for unified processing by the terminal. The disadvantage of this method is that the monitoring equipment itself has a certain cost, and it needs to be maintained during use, which makes the cost of its installation and maintenance too high. Corresponding to intrusive load monitoring is non-intrusive load monitoring. The biggest advantage of non-int...

AI Technical Summary

Problems solved by technology

The traditional neural network-based load identification method often uses a single network, which does not perform well in feature extraction of samples, and takes a long time to train the network

The main reason is that the use of loads is time-sequential, and there is a connection between the loads, which leads to the problem that the recognition effect is reduced due to the introduction of harmonics, a feature of high-dimensional information content and imbalance.

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