Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning

Yang Deng; Yaohui Liu; Rui Liang; Donghua Xie; Dafang Zhao; Ittetsu Taniguchi; Samson Tai; Dan Wang

doi:10.1145/3671127.3698705

Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning

Yang Deng, Yaohui Liu, Rui Liang, Donghua Xie, Dafang Zhao, Ittetsu Taniguchi, Samson Tai, Dan Wang

Department of Accountancy, Economics and Finance

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

This paper proposes a new framework to provide the ML forecasting model for model predictive control (MPC) in building HVAC systems. Buildings typically encompass multiple contexts, such as different types of rooms, each with distinct requirements for the ML models used in MPC. However, developing customized models requires significant effort. The proposed solution addresses this challenge through ensemble learning techniques, which involve grouping a set of existing pre-trained models to construct a new model tailored to the target context. This work employs a Bayesian Optimization algorithm, with the pre-trained models supported by an established AI platform in the building sector. On-site experimental results from two case studies demonstrate that the proposed solution reduces energy consumption by 7.96 kWh (52.4%) compared to using a single forecasting model.

Original language	English
Title of host publication	BuildSys '24
Subtitle of host publication	Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	231–232
Number of pages	2
ISBN (Print)	9798400707063
DOIs	https://doi.org/10.1145/3671127.3698705
Publication status	Published - 29 Oct 2024
Event	11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation - Hangzhou, China Duration: 7 Nov 2024 → 8 Nov 2024 https://dl.acm.org/doi/proceedings/10.1145/3671127 (Conference proceedings)

Publication series

Name	Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Conference

Conference	11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
Country/Territory	China
City	Hangzhou
Period	7/11/24 → 8/11/24
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3671127 (Conference proceedings)

User-Defined Keywords

Model ensemble
automation
smart building
HVAC control

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3671127.3698705

Cite this

Deng, Y., Liu, Y., Liang, R., Xie, D., Zhao, D., Taniguchi, I., Tai, S., & Wang, D. (2024). Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning. In BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 231–232). (Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation). Association for Computing Machinery (ACM). https://doi.org/10.1145/3671127.3698705

Deng, Yang ; Liu, Yaohui ; Liang, Rui et al. / Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning. BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York : Association for Computing Machinery (ACM), 2024. pp. 231–232 (Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation).

@inproceedings{5415cec32ed34d6990128a69714bf4a5,

title = "Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning",

abstract = "This paper proposes a new framework to provide the ML forecasting model for model predictive control (MPC) in building HVAC systems. Buildings typically encompass multiple contexts, such as different types of rooms, each with distinct requirements for the ML models used in MPC. However, developing customized models requires significant effort. The proposed solution addresses this challenge through ensemble learning techniques, which involve grouping a set of existing pre-trained models to construct a new model tailored to the target context. This work employs a Bayesian Optimization algorithm, with the pre-trained models supported by an established AI platform in the building sector. On-site experimental results from two case studies demonstrate that the proposed solution reduces energy consumption by 7.96 kWh (52.4%) compared to using a single forecasting model.",

keywords = "Model ensemble, automation, smart building, HVAC control",

author = "Yang Deng and Yaohui Liu and Rui Liang and Donghua Xie and Dafang Zhao and Ittetsu Taniguchi and Samson Tai and Dan Wang",

note = "Publisher Copyright: {\textcopyright} 2024 Association for Computing Machinery. Funding Information: Dan Wang{\textquoteright}s work is supported by RGC GRF 15200321, 15201322, 15230624, RGC-CRF C5018-20G, ITC ITF-ITS/056/22MX, and PolyU 1-CDKK, G-SAC8.; 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation ; Conference date: 07-11-2024 Through 08-11-2024",

year = "2024",

month = oct,

day = "29",

doi = "10.1145/3671127.3698705",

language = "English",

isbn = "9798400707063",

series = "Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation",

publisher = "Association for Computing Machinery (ACM)",

pages = "231–232",

booktitle = "BuildSys '24",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/3671127",

}

Deng, Y, Liu, Y, Liang, R, Xie, D, Zhao, D, Taniguchi, I, Tai, S & Wang, D 2024, Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning. in BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Association for Computing Machinery (ACM), New York, pp. 231–232, 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Hangzhou, China, 7/11/24. https://doi.org/10.1145/3671127.3698705

Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning. / Deng, Yang; Liu, Yaohui; Liang, Rui et al.
BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York: Association for Computing Machinery (ACM), 2024. p. 231–232 (Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning

AU - Deng, Yang

AU - Liu, Yaohui

AU - Liang, Rui

AU - Xie, Donghua

AU - Zhao, Dafang

AU - Taniguchi, Ittetsu

AU - Tai, Samson

AU - Wang, Dan

N1 - Publisher Copyright: © 2024 Association for Computing Machinery. Funding Information: Dan Wang’s work is supported by RGC GRF 15200321, 15201322, 15230624, RGC-CRF C5018-20G, ITC ITF-ITS/056/22MX, and PolyU 1-CDKK, G-SAC8.

PY - 2024/10/29

Y1 - 2024/10/29

N2 - This paper proposes a new framework to provide the ML forecasting model for model predictive control (MPC) in building HVAC systems. Buildings typically encompass multiple contexts, such as different types of rooms, each with distinct requirements for the ML models used in MPC. However, developing customized models requires significant effort. The proposed solution addresses this challenge through ensemble learning techniques, which involve grouping a set of existing pre-trained models to construct a new model tailored to the target context. This work employs a Bayesian Optimization algorithm, with the pre-trained models supported by an established AI platform in the building sector. On-site experimental results from two case studies demonstrate that the proposed solution reduces energy consumption by 7.96 kWh (52.4%) compared to using a single forecasting model.

AB - This paper proposes a new framework to provide the ML forecasting model for model predictive control (MPC) in building HVAC systems. Buildings typically encompass multiple contexts, such as different types of rooms, each with distinct requirements for the ML models used in MPC. However, developing customized models requires significant effort. The proposed solution addresses this challenge through ensemble learning techniques, which involve grouping a set of existing pre-trained models to construct a new model tailored to the target context. This work employs a Bayesian Optimization algorithm, with the pre-trained models supported by an established AI platform in the building sector. On-site experimental results from two case studies demonstrate that the proposed solution reduces energy consumption by 7.96 kWh (52.4%) compared to using a single forecasting model.

KW - Model ensemble

KW - automation

KW - smart building

KW - HVAC control

U2 - 10.1145/3671127.3698705

DO - 10.1145/3671127.3698705

M3 - Conference proceeding

SN - 9798400707063

T3 - Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

SP - 231

EP - 232

BT - BuildSys '24

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Y2 - 7 November 2024 through 8 November 2024

ER -

Deng Y, Liu Y, Liang R, Xie D, Zhao D, Taniguchi I et al. Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning. In BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York: Association for Computing Machinery (ACM). 2024. p. 231–232. (Proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation). doi: 10.1145/3671127.3698705

Towards Machine Learning-based Model Predictive Control for HVAC Control in Multi-Context Buildings at Scale via Ensemble Learning

Abstract

Publication series

Conference

User-Defined Keywords

UN SDGs

Access to Document

Fingerprint

Cite this