Optimal partitioning of multi-thermal zone buildings for decentralized control

TL;DR

This paper presents a systematic optimization-based method for partitioning multi-thermal zone buildings to enable effective decentralized control, using graph-based modeling and clustering techniques tested on small and large-scale examples.

Contribution

It introduces a novel graph-based approach combined with stochastic and robust optimization for optimal thermal zone partitioning in buildings.

Findings

Successfully tested on small-scale building for partitioning success rate.

Predicted optimal partitions for large-scale building.

Provides a systematic and potentially optimal solution compared to existing methods.

Abstract

In this paper, we develop an optimization-based systematic approach for the challenging, less studied, and important problem of optimal partitioning of multi-thermal zone buildings for the decentralized control. The proposed method consists of (i) construction of a graph-based network to quantitatively characterize the thermal interaction level between neighbor zones, and (ii) the application of two different approaches for optimal clustering of the resulting network graph: stochastic optimization and robust optimization. The proposed method was tested on two case studies: a 5-zone building (a small-scale example) which allows one to consider all possible partitions to assess the success rate of the developed method; and a 20-zone building (a large-scale example) for which the developed method was used to predict the optimal partitioning of the thermal zones. Compared to the existing literature, our approach provides a systematic and potentially optimal solution for the considered problem.