Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a transfer learning based deep network framework for calculating virtual battery state of a given ensemble of flexible thermostatic loads, from the available end-use measurements. This proposed framework extracts first order virtual battery model parameters for the given ensemble. We illustrate the effectiveness of this novel framework on different ensembles of ACs and WHs.

本文提出了一种基于迁移学习的深度网络框架，用于从可用的末端使用测量值计算给定灵活恒温负载集合的虚拟电池状态，并从中提取一阶虚拟电池模型参数，以解决在实际应用中仅有有关末端使用测量的信息而无法获得单个负载模型和参数的问题。在不同的空调和热水器集合上，验证了该框架的有效性。

基于迁移学习的堆叠自编码器虚拟电池参数识别