Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning
https://orcid.org/0009-0009-3323-6451
https://orcid.org/0009-0008-7777-5273
https://orcid.org/0000-0003-0660-7017
https://orcid.org/0000-0001-6035-1759
Abstract Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.
Highlights Data-driven hierarchical control configuration for optimizing multi-zone HVAC systems. Proposal of a DPC configuration using the Koopman operator and neural network. Imposing iISS constraint on the predictor training algorithm. Proposed method validation using the simulated three-zone HVAC system in TRNSYS. Significant energy savings compared to the baseline controller and another DPC method.
Graphical abstract Display Omitted
Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning
https://orcid.org/0009-0009-3323-6451
https://orcid.org/0009-0008-7777-5273
https://orcid.org/0000-0003-0660-7017
https://orcid.org/0000-0001-6035-1759
Abstract Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.
Highlights Data-driven hierarchical control configuration for optimizing multi-zone HVAC systems. Proposal of a DPC configuration using the Koopman operator and neural network. Imposing iISS constraint on the predictor training algorithm. Proposed method validation using the simulated three-zone HVAC system in TRNSYS. Significant energy savings compared to the baseline controller and another DPC method.
Graphical abstract Display Omitted
Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning
https://orcid.org/0009-0009-3323-6451
https://orcid.org/0009-0008-7777-5273
https://orcid.org/0000-0003-0660-7017
https://orcid.org/0000-0001-6035-1759
Abstract Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method’s superiority.
Highlights Data-driven hierarchical control configuration for optimizing multi-zone HVAC systems. Proposal of a DPC configuration using the Koopman operator and neural network. Imposing iISS constraint on the predictor training algorithm. Proposed method validation using the simulated three-zone HVAC system in TRNSYS. Significant energy savings compared to the baseline controller and another DPC method.
Graphical abstract Display Omitted
Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning
Soleimani, Mohammadjavad (Autor:in) / Irani, Fatemeh Negar (Autor:in) / Yadegar, Meysam (Autor:in) / Davoodi, Mohammadreza (Autor:in)
Building and Environment ; 248
24.11.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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