Abstract
In order to improve the accuracy of non-invasive power load monitoring and shorten the monitoring time, a new non-invasive power load monitoring method based on machine learning is proposed. Firstly, the power load data is collected by sensors and normalized. Secondly, based on the normalized results of power load data, the RBF neural network model in machine learning is used for iterative training to extract power load characteristics. Finally, according to the characteristics of power load, the hidden Markov model is used to transform the non-invasive power load monitoring problem into a decoding problem, and the improved Viterbi algorithm is used to solve the hidden Markov model, and the non-invasive power load monitoring is completed according to the obtained state sequence.
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