Power abnormal fluctuation detection and prediction calculation method based on integrated algorithm

Abstract

The invention relates to power abnormal fluctuation detection and prediction calculation method based on mutational site analysis and a hidden Markov model integration algorithm. The method comprises the following steps of: acquiring power consumption data as data of a time sequence input for detection and prediction; mutational site analysis: realizing the mutational site analysis of abnormal fluctuation through Bayesian inference by using the collected electricity consumption data; mutational site data prediction: processing information with correlation in time by utilizing a hidden Markov model, processing information aiming at a time sequence, obtaining likelihood parameters by adopting a hidden Markov forward algorithm and a Baum-Welch algorithm, obtaining mutational site prediction data, and obtaining a maximum likelihood value corresponding to a sliding window through training features. The improvement is carried out based on abrupt change point analysis and a basic algorithm of a hidden Markov model, electric power consumption can be detected in real time, a reasonable range of an electric power consumption interval at a future moment can be predicted and calculated, and it is judged that the electric power consumption is abnormal if the range is exceeded.

Description

technical field [0001] This application relates to the intelligent Internet of Things (AIoT) technology, especially the detection and prediction calculation method of abnormal power fluctuations of the intelligent Internet of Things based on the integration algorithm of mutation point analysis and hidden Markov model. Background technique [0002] It is a common method in industrial and engineering projects to judge whether instruments and equipment are operating normally through abnormal power fluctuations. Therefore, improving the accuracy of abnormal power fluctuation detection is of great significance for ensuring the normal operation of instruments and equipment and sub-item measurement of building energy consumption. [0003] With the rise of artificial intelligence (AI) algorithms in recent years, many scholars have begun to explore the application of time series algorithms in power load forecasting, but there are few studies on the application of AI algorithms in the...

AI Technical Summary

Problems solved by technology

[0005] 1. There is no clear definition index for power abnormalities;

[0006] 2. There is a lack of labeled data for power abnormalities;

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