A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings
Abstract Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for o...
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doaj-8ba547d4df6544ef822bbe4b16911f242021-09-26T11:15:58ZengSpringerOpenEnergy Informatics2520-89422021-09-014S211910.1186/s42162-021-00153-9A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildingsAnders Clausen0Krzysztof Arendt1Aslak Johansen2Fisayo Caleb Sangogboye3Mikkel Baun Kjærgaard4Christian T. Veje5Bo Nørregaard Jørgensen6Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkCenter for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkSoftware engineering unit, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkCenter for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkSoftware engineering unit, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkCenter for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkCenter for Energy Informatics, Maersk Mc-Kinney Moller Institute, University of Southern DenmarkAbstract Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort.https://doi.org/10.1186/s42162-021-00153-9Model predictive controlGenetic algorithmSoftware framework |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anders Clausen Krzysztof Arendt Aslak Johansen Fisayo Caleb Sangogboye Mikkel Baun Kjærgaard Christian T. Veje Bo Nørregaard Jørgensen |
spellingShingle |
Anders Clausen Krzysztof Arendt Aslak Johansen Fisayo Caleb Sangogboye Mikkel Baun Kjærgaard Christian T. Veje Bo Nørregaard Jørgensen A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings Energy Informatics Model predictive control Genetic algorithm Software framework |
author_facet |
Anders Clausen Krzysztof Arendt Aslak Johansen Fisayo Caleb Sangogboye Mikkel Baun Kjærgaard Christian T. Veje Bo Nørregaard Jørgensen |
author_sort |
Anders Clausen |
title |
A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings |
title_short |
A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings |
title_full |
A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings |
title_fullStr |
A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings |
title_full_unstemmed |
A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings |
title_sort |
digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings |
publisher |
SpringerOpen |
series |
Energy Informatics |
issn |
2520-8942 |
publishDate |
2021-09-01 |
description |
Abstract Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort. |
topic |
Model predictive control Genetic algorithm Software framework |
url |
https://doi.org/10.1186/s42162-021-00153-9 |
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