DATA-CENTRIC MODEL PREDICTIVE CONTROL FOR ACMV SYSTEMS IN TROPICAL OFFICE BUILDINGS

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.