A real-time energy consumption abnormity detection method with college building structure characteristics being combined

Abstract

The invention belongs to the technical field of real-time abnormity detection, and specially relates to a real-time energy consumption abnormity detection method with college building structure characteristics being combined. Energy consumption modes of buildings are identified in a self-adaptive manner through a SA-DBSCAN algorithm, and a decision tree of the energy consumption modes is constructed through a C4.5 algorithm; if an acquisition child node has no machine fault, real-time energy consumption is processed first according to change of a parent-child structure of a building node, then, the type of the real-time energy consumption is obtained according to the decision tree, and abnormity is detected through an LOF algorithm; if abnormity is detected, the decision tree is adjusted based on whether a boundary threshold value is reached; and normal energy consumption data increment is updated to the energy consumption modes of the buildings in batch, and an abnormity detection model is adjusted dynamically according to whether the type of energy consumption mode is changed. The method can effectively detect abnormal energy consumption and dynamically adjust the detection model to reduce misjudgment.

Description

technical field [0001] The invention belongs to the technical field of real-time anomaly detection, and in particular relates to a real-time energy consumption anomaly detection method combined with structural features of college buildings. Background technique [0002] Many universities have established energy consumption monitoring platforms and have collected a large amount of energy consumption data, which contains important information during building operation. In particular, real-time abnormal detection of energy consumption is conducive to discovering unreasonable aspects of building use and management, and timely adjustment can achieve the purpose of energy saving. At present, researchers have proposed many intelligent energy consumption anomaly detection methods, but they all use a fixed model for static analysis without considering environmental changes. The energy consumption environment of university buildings is relatively dynamic, and there are many types of ...

AI Technical Summary

Problems solved by technology

The addition and deletion of smart meters is the process of continuous improvement of the energy-saving supervision platform, and the practical significance of detecting such anomalies is not great

Moreover, this kind of situation is relatively common, and the detection of mixed together increases the difficulty of analysis and interpretation of other abnormalities, so it should be taken into consideration

Some smart meter nodes under the building node sometimes have empty values. This situation is likely to be caused by a machine failure in the collection machine of the smart meter

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