Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder
This work addresses a practical challenge in energy management by enabling virtual battery modeling from limited real-world data, though it appears incremental as it builds on existing virtual battery concepts with a new computational approach.
The paper tackles the problem of estimating virtual battery parameters for aggregated thermostatic loads without requiring detailed first-principle models, by proposing a transfer learning-based deep network framework that uses end-use measurements like power consumption and temperature, and demonstrates its effectiveness on ensembles of air conditioners and water heaters.
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.