Abstract
The analysis of building abnormal energy consumption is of great significance to the effective energy saving of buildings. To apply the relationship between the running status of building equipment and energy consumption to the diagnosis of abnormal energy consumption, an abnormal diagnosis method of building energy consumption based on the improved Apriori association rules is proposed. An improved Apriori algorithm is proposed for building energy consumption data with a large amount of data and multi-value attributes. The improved Apriori algorithm determines whether different attribute values of the same attribute data are in advance when generating candidate sets, reduces the number of comparisons, and improves the algorithm efficiency. By analyzing the abnormal energy consumption of the chiller in the refrigeration station of a commercial building, the superiority of the improved Apriori algorithm is proved, and the abnormal energy consumption is found, which verifies the feasibility and practicability of the proposed method.
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