Enhancement of Building Energy Consumption Using a Digital Twin based Neural Network Model Predictive Control

Agouzoul, Abdelali; Simeu, Emmanuel; Tabaa, Mohamed

doi:10.1109/iccad57653.2023.10152308

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Cited by 4 publications

References 27 publications

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Towards a classification of the building stock in terms of potential energy savings through advanced control

Wagner,

Ruellan,

Heinrich

et al. 2024

Energy and Buildings

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Towards a classification of the building stock in terms of potential energy savings through advanced control

Wagner,

Ruellan,

Heinrich

et al. 2024

Energy and Buildings

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A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting

Boukaf,

Fadli,

Meskin

2024

IEEE Access

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DEMSA: a DT-enabled Middleware for Self-adaptive Smart Spaces

Ma,

Bouloukakis,

Kattepur

et al. 2023

Proceedings of the 1st International Workshop on Middleware for Digital Twin

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Heating, Ventilation, and Air Conditioning (HVAC) systems account for a significant portion of energy consumption within buildings. In order to balance the effect of thermal comfort vis-a-vis energy savings, HVAC control strategies have been proposed. However, the strategies are static and do not take into account dynamic changes of consumers, hence creating sub-optimal outcomes. This paper proposes DEMSA, a Digital Twin (DT)-enabled middleware for the self-adaptation of smart buildings. The DEMSA middleware interconnects and coordinates intelligent data exchange between the building edge server, digital twin and Artificial Intelligence (AI) planning nodes in order to invoke appropriate strategies. Moreover, DEMSA is paired with a selfadaptive mechanism that can detect the anomaly of generated planning and adaptively modify it. This process ensures balancing building energy consumption and thermal comfort requirements, without human intervention. The DEMSA middleware is described over a real smart space scenario.

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Enhancement of Building Energy Consumption Using a Digital Twin based Neural Network Model Predictive Control

Cited by 4 publications

References 27 publications

Towards a classification of the building stock in terms of potential energy savings through advanced control

Towards a classification of the building stock in terms of potential energy savings through advanced control

A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting

DEMSA: a DT-enabled Middleware for Self-adaptive Smart Spaces

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