A data-driven evaluating method on the defrosting effect of the air source heat pump system in Beijing

The current demand-based defrosting initiation strategies of air source heat pumps (ASHPs) faces difficulty in achieving a balance between cost and robustness. Therefore, an efficient and innovative demand-based defrosting initiation strategy is proposed in this paper, based on the learned the degradation of heating capacity (DHC) method using data-driven model. Firstly, the DHC method is developed using the initially installed sensors without additional sensors to identify the frosty state. Subsequently, the fully connected neural network (FNN) model is established for predicting frosty state using field measured data from the ASHP system. Finally, the effectiveness of the defrosting initiation strategy is further validated through practical testing. The results demonstrate that the DHC method provides a reliable database for training the FNN model, resulting in an impressive accuracy of 91.43% for predicting the frosty state in the testing set. By adopting the innovative demand-based defrosting initiation strategies, the defrosting frequency, heating loss, and power consumption are respectively decreased by 66.3%, 2.1%, and 6.0% with the SCOP enhanced by 8.6% during a heating season. The promising results highlight that the proposed strategy effectively reduces costs while ensuring the efficiency of demand-based control.

Automated summary

An efficient and innovative demand-based defrosting initiation strategy is proposed in this paper, based on the learned the degradation of heating capacity (DHC) method using data-driven model. By utilizing sensor data and a neural network model, the strategy effectively reduces defrosting frequency, heating loss, and power consumption while ensuring the efficiency of demand-based control. The results demonstrate a significant improvement in defrosting control efficiency.