Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control
The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is pre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Salakij, Saran [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives - Plonowska, Karolina A. ELSEVIER, 2018, an international journal of research applied to energy efficiency in the built environment, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:133 ; year:2016 ; day:1 ; month:12 ; pages:345-358 ; extent:14 |
| Links: |
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| DOI / URN: |
10.1016/j.enbuild.2016.09.044 |
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ELV019432739 |
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| 520 | |a The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. | ||
| 520 | |a The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. | ||
| 650 | 7 | |a Building energy model |2 Elsevier | |
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| 700 | 1 | |a Paolucci, Samuel |4 oth | |
| 700 | 1 | |a Antsaklis, Panos |4 oth | |
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10.1016/j.enbuild.2016.09.044 doi GBVA2016013000015.pica (DE-627)ELV019432739 (ELSEVIER)S0378-7788(16)30890-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 610 VZ 44.94 bkl Salakij, Saran verfasserin aut Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. Building energy model Elsevier Hygrothermal model Elsevier Model-based control Elsevier Energy efficiency Elsevier Thermal model Elsevier Yu, Na oth Paolucci, Samuel oth Antsaklis, Panos oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:133 year:2016 day:1 month:12 pages:345-358 extent:14 https://doi.org/10.1016/j.enbuild.2016.09.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 133 2016 1 1201 345-358 14 045F 690 |
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10.1016/j.enbuild.2016.09.044 doi GBVA2016013000015.pica (DE-627)ELV019432739 (ELSEVIER)S0378-7788(16)30890-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 610 VZ 44.94 bkl Salakij, Saran verfasserin aut Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. Building energy model Elsevier Hygrothermal model Elsevier Model-based control Elsevier Energy efficiency Elsevier Thermal model Elsevier Yu, Na oth Paolucci, Samuel oth Antsaklis, Panos oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:133 year:2016 day:1 month:12 pages:345-358 extent:14 https://doi.org/10.1016/j.enbuild.2016.09.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 133 2016 1 1201 345-358 14 045F 690 |
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10.1016/j.enbuild.2016.09.044 doi GBVA2016013000015.pica (DE-627)ELV019432739 (ELSEVIER)S0378-7788(16)30890-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 610 VZ 44.94 bkl Salakij, Saran verfasserin aut Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. Building energy model Elsevier Hygrothermal model Elsevier Model-based control Elsevier Energy efficiency Elsevier Thermal model Elsevier Yu, Na oth Paolucci, Samuel oth Antsaklis, Panos oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:133 year:2016 day:1 month:12 pages:345-358 extent:14 https://doi.org/10.1016/j.enbuild.2016.09.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 133 2016 1 1201 345-358 14 045F 690 |
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10.1016/j.enbuild.2016.09.044 doi GBVA2016013000015.pica (DE-627)ELV019432739 (ELSEVIER)S0378-7788(16)30890-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 610 VZ 44.94 bkl Salakij, Saran verfasserin aut Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. Building energy model Elsevier Hygrothermal model Elsevier Model-based control Elsevier Energy efficiency Elsevier Thermal model Elsevier Yu, Na oth Paolucci, Samuel oth Antsaklis, Panos oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:133 year:2016 day:1 month:12 pages:345-358 extent:14 https://doi.org/10.1016/j.enbuild.2016.09.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 133 2016 1 1201 345-358 14 045F 690 |
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10.1016/j.enbuild.2016.09.044 doi GBVA2016013000015.pica (DE-627)ELV019432739 (ELSEVIER)S0378-7788(16)30890-8 DE-627 ger DE-627 rakwb eng 690 690 DE-600 610 VZ 44.94 bkl Salakij, Saran verfasserin aut Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. Building energy model Elsevier Hygrothermal model Elsevier Model-based control Elsevier Energy efficiency Elsevier Thermal model Elsevier Yu, Na oth Paolucci, Samuel oth Antsaklis, Panos oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:133 year:2016 day:1 month:12 pages:345-358 extent:14 https://doi.org/10.1016/j.enbuild.2016.09.044 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 133 2016 1 1201 345-358 14 045F 690 |
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Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control |
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The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. |
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The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. |
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The simultaneous heat and moisture transfer in the building envelope has an important influence on the indoor climate and the overall thermal performance of buildings. In this work, the development of a Building Energy Analysis Model (BEAM) predicting whole building heat and moisture transfer is presented. The coupled heat and moisture transfer model takes into account most of the main hygrothermal effects in buildings. The coupled system model is implemented in Matlab, and verified with EnergyPlus. Furthermore, BEAM is reduced via a physically based model order reduction to a lower order system model (Re-BEAM) to be easily integrated with a control algorithm. By utilizing Re-BEAM, a Model-Based Predictive Control (MBPC) method is developed to incorporate critical building information into control algorithms, such that the building energy consumption is minimized while comfort conditions are maintained. The resulting optimal setpoint schedule can be applied on any HVAC system. Simulation results of a building structure demonstrate the superiority in terms of energy and peak load reductions compared with traditional constant control methods and control methods that use a occupants-varying temperature schedule. |
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