Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach

TL;DR

This paper introduces a primal-dual contextual Bayesian optimization method for tuning building thermal controllers, effectively reducing energy use and discomfort while adapting to changing constraints in real-time.

Contribution

It presents a novel data-driven optimization approach that handles constraints and time-varying thresholds for building thermal control, outperforming existing methods.

Findings

PDCBO saves up to 4.7% energy compared to other Bayesian methods.

It maintains thermal discomfort below thresholds on average.

It adapts to changing tolerable thresholds automatically.

Abstract

We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold. We formulate it as an online constrained black-box optimization problem where, on each day, we observe some relevant environmental context and adaptively select the controller parameters. In this paper, we propose to use a data-driven Primal-Dual Contextual Bayesian Optimization (PDCBO) approach to solve this problem. In a simulation case study on a single room, we apply our algorithm to tune the parameters of a Proportional Integral (PI) heating controller and the pre-heating time. Our results show that PDCBO can save up to 4.7% energy consumption compared to other state-of-the-art Bayesian optimization-based methods while keeping the daily thermal discomfort below the given tolerable threshold on average. Additionally, PDCBO can automatically track time-varying tolerable thresholds while existing methods fail to do so. We then study an alternative constrained tuning problem where we aim to minimize the thermal discomfort with a given energy budget. With this formulation, PDCBO reduces the average discomfort by up to 63% compared to state-of-the-art safe optimization methods while keeping the average daily energy consumption below the required threshold.