Association Rules Mining of Solar-assisted Air Source Heat Pump Systems

With the rapid development of big data and machine learning algorithms, the application of data mining technology to mine hidden information in various fields has become increasingly widespread, which also plays an important role in the field of air conditioning. This paper uses association rule mining algorithms to explore the association rules existing in the operation data of solar-assisted air source heat pump air conditioning systems, and combines professional knowledge for analysis. The paper mainly introduces the operation status of the system, data acquisition and storage, then uses the IsolationForest algorithm to detect outlier, uses the equal-frequency method for data discretization and assigns numbers to the discrete intervals. Finally, the Apriori algorithm is used to mine the preprocessed data. The mining results find that the working status of circulating water pump 1 is subject to the start-stop control of the unit's compressor. The analysis results show that association rule mining has important guiding significance for the operation and adjustment of the system.