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Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage

Journal Article


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Abstract


  • This paper describes the development, implementation and experimental investigation of a Hybrid Model

    Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning

    (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the

    thermal energy management of a residential building is presented to optimise the scheduling of the available

    thermal energy resources to meet a comfort objective. The system has a hybrid nature with both

    continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized

    in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and

    a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating

    mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed

    control strategy has been practically implemented on the Building Management and Control System

    (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system

    comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal

    storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The

    simulation and experimental results demonstrated the high performance achievable using an HMPC

    approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient

    selection of the appropriate operating modes to optimally manage thermal energy of the house.

Publication Date


  • 2017

Citation


  • Fiorentini, M., Wall, J., Ma, Z., Braslavsky, J. H. & Cooper, P. (2017). Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage. Applied Energy, 187 465-479.

Scopus Eid


  • 2-s2.0-84998814292

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7349&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6319

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 465

End Page


  • 479

Volume


  • 187

Place Of Publication


  • United Kingdom

Abstract


  • This paper describes the development, implementation and experimental investigation of a Hybrid Model

    Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning

    (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the

    thermal energy management of a residential building is presented to optimise the scheduling of the available

    thermal energy resources to meet a comfort objective. The system has a hybrid nature with both

    continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized

    in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and

    a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating

    mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed

    control strategy has been practically implemented on the Building Management and Control System

    (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system

    comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal

    storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The

    simulation and experimental results demonstrated the high performance achievable using an HMPC

    approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient

    selection of the appropriate operating modes to optimally manage thermal energy of the house.

Publication Date


  • 2017

Citation


  • Fiorentini, M., Wall, J., Ma, Z., Braslavsky, J. H. & Cooper, P. (2017). Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage. Applied Energy, 187 465-479.

Scopus Eid


  • 2-s2.0-84998814292

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7349&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6319

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 465

End Page


  • 479

Volume


  • 187

Place Of Publication


  • United Kingdom