Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder

TL;DR

This paper introduces a transfer learning-based deep autoencoder framework to estimate virtual battery parameters of thermostatic load ensembles using only end-use measurements, bypassing the need for detailed load models.

Contribution

It presents a novel transfer learning approach with stacked autoencoders to identify virtual battery parameters from limited measurement data.

Findings

Effective parameter estimation on AC and water heater ensembles

Reduces reliance on detailed load models

Demonstrates applicability to real-world measurement data

Abstract

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.