On-line diagnostic method for abnormal energy consumption branch of building

Abstract

The invention discloses an on-line diagnostic method for an abnormal energy consumption branch of a building. The method comprises the following steps of: (1) circularly detecting all branches of an entire building according to a preset branch detection sequence; (2) determining detection reference time, and if a branch is detected for the first time, reading energy consumption information of thebranch from an energy consumption information management database and reconstructing to a phase space according to a phase space theory; and (3) providing branch abnormal alarm information corresponding to the energy consumption data when the detected energy consumption data is abnormal. The invention provides the method for performing data mining from a great capacity of energy consumption information of the building branch and discovering abnormal energy consumption data; and by the method, dynamic modeling and real-time abnormal data judgment can be realized and an adaptive diagnostic model is established by scrolling correction. The method solves the problem of processing the non-linear abnormal data detection by other methods based on a phase space reconstruction theory and classification technology by using a kernel function.

Description

technical field [0001] The online diagnosis method for abnormal building energy consumption branches proposed by the present invention is based on the phase space reconstruction theory and a class of classification technology, and realizes the detection of abnormal data of building energy consumption branches, which belongs to the field of green energy saving technology. Background technique [0002] With the implementation of national energy conservation and emission reduction policies, the data of energy consumption branches of many office buildings and large public buildings in various regions have been uploaded to the data center, mainly to realize the sub-item measurement of energy consumption and the analysis of sub-item energy consumption data. item statistics display. Building branch road energy consumption data is usually massive information, and the data contains a lot of information. It is of great significance to mine the hidden useful information and provide dec...

AI Technical Summary

Problems solved by technology

Traditional model methods are effective in detecting outliers in linear time series, and the model has good interpretability, but these methods are still difficult to apply to outlier detection in more complex nonlinear time series

[0003] Traditionally, some mathematical methods can be used to analyze abnormal energy consumption of building branch roads, such as statistical methods, deviation methods, density methods, etc. A common shortcoming of all these data detection methods is that although building branch roads can be more or less detected road abnormal energy consumption data, but because the algorithm is considered more from a mathematical point of view, the model dependence is too strong, and it lacks wide application value, so it is only studied by a small number of researchers

[0004] Building branch abnormalities are often realized through manual trend analysis or using some statistical tools plus artificial analysis, which is very inefficient. Faced with a building with dozens of energy consumption branches, manual analysis is difficult to conduct a comprehensive analysis, let alone It is possible to realize the online diagnosis of abnormal energy consumption branches of buildings

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