A load-based testing methodology has recently been developed for laboratory testing of residential cooling equipment with its integrated controls that employs a model to emulate building dynamics and their interaction with equipment controls. This approach is part of a new performance rating standard that utilizes a bin method to estimate a seasonal energy efficiency (CSA EXP-07, 2019; Cheng et al., 2021). However, a “holy grail” for future equipment performance rating is to be able to map equipment performance using load-based testing results and then implement the map as a "model" in building energy simulations to generate seasonal performance ratings that are specific to various building and climate types. With this goal in mind, this paper presents a performance mapping methodology that incorporates a “gray-box” model structure that uses inputs that are consistent with building energy simulation programs (sensible cooling load and equipment inlet conditions) and outputs key performance metrics (total sensible and latent cooling rates, power consumption, and COP). A strategy and a test matrix for training the model with a relatively small number of testing points were established by developing a successive optimization approach to identify the best 12 test conditions to apply for training from a larger set of available data. In order to develop, demonstrate, and validate the modeling and training approach, load-based laboratory testing was set up within psychrometric chambers and testing was performed to generate 39 quasi-steady-state data points over a range of loads and boundary conditions with a test unit operating with its normal integrated controls. The best model trained using the optimal training subset of 12 data points was able to represent the equipment performance across the operating envelope within $\pm 10\%$.

最近开发了一种基于负载的测试方法，用于住宅冷却设备的实验室测试，其集成控制采用模型来模拟建筑动态及其与设备控制的交互。这种方法是新的性能评级标准的一部分，该标准利用 bin 方法来估计季节性能源效率（CSA EXP-07，2019 年；Cheng 等人，2021 年）。然而，未来设备性能评级的“圣杯”是能够使用基于负载的测试结果映射设备性能，然后将该映射作为建筑能源模拟中的“模型”来生成特定于各种的季节性性能评级。建筑和气候类型。带着这个目标，本文提出了一种性能映射方法，该方法结合了“灰盒”模型结构，该结构使用与建筑能源模拟程序一致的输入（显冷负荷和设备入口条件）并输出关键性能指标（总显冷和潜冷速率，功耗和 COP）。通过开发连续优化方法来确定最佳的 12 个测试条件以从更大的可用数据集中应用于训练，从而建立了用于训练具有相对较少测试点数的模型的策略和测试矩阵。为了开发、演示和验证建模和培训方法，基于负载的实验室测试设置在空气湿度室中，并通过测试单元在正常集成控制下运行，在一系列负载和边界条件下生成 39 个准稳态数据点。使用 12 个数据点的最佳训练子集训练的最佳模型能够代表整个操作范围内的设备性能$\pm 10\%$.