Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/235010
Title: DATA-CENTRIC MODEL PREDICTIVE CONTROL FOR ACMV SYSTEMS IN TROPICAL OFFICE BUILDINGS
Authors: ZHAN SICHENG
ORCID iD:   orcid.org/0000-0002-9872-8555
Keywords: model predictive control, data management, smart buildings, energy performance, control-oriented model
Issue Date: 8-Aug-2022
Citation: ZHAN SICHENG (2022-08-08). DATA-CENTRIC MODEL PREDICTIVE CONTROL FOR ACMV SYSTEMS IN TROPICAL OFFICE BUILDINGS. ScholarBank@NUS Repository.
Abstract: The potential of using Model Predictive Control (MPC) to improve building operation has been shown in many studies. Unfortunately, real-world applications are still restricted by the high implementation cost and the unguaranteed profitability. In the traditional paradigm of ``model-centric'' MPC, most effort is devoted to constructing the control-oriented model given specific building properties and data availability. Due to the significant heterogeneity among buildings, the results are hardly reproducible, and a high level of customization is required for each new building. To address this issue, a new “data-centric'' approach for MPC, which starts with control-oriented data curation that acquires the necessary and cost-effective data concerning the intended control purpose and the building characteristics. The foundation of data-centric MPC is a standardized framework to quantify the data requirements and the established relationships between data usage and control performance. Such an end-to-end framework promotes actual MPC applications with controllable costs and reliable outcomes.
URI: https://scholarbank.nus.edu.sg/handle/10635/235010
Appears in Collections:Ph.D Theses (Open)

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