The annual performance factor (APF) is a new performance index of heat pump type air conditioners (HPACs) based on multiple testing conditions which may result in orders of magnitude larger workload in the product development for enhancing the APF to be close to the maximal value. The development process can be accelerated if the maximum APF can be predicted. The purpose of this study is to present a fast prediction method with a high precision for the maximum APF of HPACs based on a single group of experimental data. The idea of achieving fast prediction is to establish a simplified lumped parameter model for the calculation of capacities and efficiencies of HPACs, and to present a simplified approximate APF correlation by eliminating all the intermediate parameters from the complicated APF equations defined in standards of HPACs. A group of experimental data is applied in the prediction model to improve the precision of the fast prediction method. Application of this method for a typical HPAC shows that the predicted maximum APF of the HPAC agrees well with the tested maximum APF, the maximum deviation of predicted capacities and efficiencies from the experimental data is within ±1.4%; and the duration of prediction for maximum APF is within 12 s on a typical personal computer, significantly saving the time of HPAC development.

年度性能因子（APF）是基于多种测试条件的热泵式空调（HPAC）的新性能指标，这可能会导致产品开发工作量增加几个数量级，从而使APF增强到接近最大值。如果可以预测最大APF，则可以加快开发过程。本研究的目的是基于一组实验数据，提出一种针对HPAC的最大APF的高精度快速预测方法。实现快速预测的想法是建立一个简化的集总参数模型，用于计算HPAC的容量和效率，并通过从HPACs标准中定义的复杂APF方程中消除所有中间参数来呈现简化的近似APF相关性。将一组实验数据应用于预测模型，以提高快速预测方法的精度。该方法在典型HPAC上的应用表明，HPAC的预测最大APF与测试的最大APF非常吻合，预测容量和效率与实验数据的最大偏差在±1.4％之内；在典型的个人计算机上，最大APF的预测持续时间在12秒以内，从而大大节省了HPAC的开发时间。