Dimension-reduced Optimization of Multi-zone Thermostatically Controlled Loads

TL;DR

This paper introduces a novel auto-encoder based dimensionality reduction technique for optimizing multi-zone thermostatically controlled loads, significantly improving computational efficiency and reducing control costs.

Contribution

It presents a new multi-task learning framework and optimization algorithms that efficiently handle high-dimensional TCL control problems through latent variable representation.

Findings

Reduces optimization dimensionality while preserving nonlinear dependencies.

Achieves higher computational efficiency compared to traditional methods.

Effectively lowers control costs in a 90-zone apartment prototype.

Abstract

This study proposes a computationally efficient method for optimizing multi-zone thermostatically controlled loads (TCLs) by leveraging dimensionality reduction through an auto-encoder. We develop a multi-task learning framework to jointly represent latent variables and formulate a state-space model based on observed TCL operation data. This significantly reduces the dimensionality of TCL variables and states while preserving critical nonlinear interdependencies in TCL control. To address various application scenarios, we introduce optimization algorithms based on system identification (OptIden) and system simulation (OptSim) tailored to the latent variable representation. These approaches employ automatic differentiation and zeroth-order techniques, respectively, for efficient implementation. We evaluate the proposed method using a 90-zone apartment prototype, comparing its performance to traditional high-dimensional optimization. Results demonstrate that our approach effectively reduces control costs while achieving significantly higher computational efficiency.