A novel defrosting initiation strategy based on convolutional neural network for air-source heat pump

Defrosting is an essential and vital part of the air source heat pump (ASHP) to restore the heating capacity and operating performance during the wintertime when the humid ambient air can cause frosting on the surface of the evaporator. A timely and effective defrosting control strategy that determines the defrosting start and exit time is critical for improving the system operating performance. However, current time-based defrosting methods that make defrosting decisions are mostly dependent on the outside ambient condition, potentially causing the mal-defrosting. The developed demand-based defrosting methods are usually unstable or costly for implementation. This paper presents a novel defrosting initiating control strategy based on the convolutional neural network (CNN) for ASHP. The CNN defrosting mechanism is based on the heat pump system's internal operating parameters rather than the outside ambient condition detection. In this paper, a CNN defrosting model is first built and then trained to learn the defrosting logic from an existing time-based method. An experiment of ASHP is conducted to acquire the dataset for CNN training. The CNN is evaluated under 6 different cases, and the maximal and minimal predicted error is 12% and 2% respectively. This demonstrates that CNN is capable to provide accurate frosting predictions and make correct defrosting initiating decisions. It can successfully capture the defrosting logic of time-based method, while avoiding the mal-defrosting caused by time-based method. (c) 2021 Published by Elsevier Ltd.

Automated summary

Defrosting is crucial for restoring heating capacity in ASHP systems during winter, but current time-based methods relying on external conditions may lead to mal-defrosting. This study introduces a novel CNN-based defrosting control strategy that accurately predicts frosting and makes correct defrosting initiation decisions.