Energy saving and indoor temperature control for an office building using tube-based robust model predictive control

Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the unc...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gao, Yuan [verfasserIn] Miyata, Shohei [verfasserIn] Akashi, Yasunori [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control

Übergeordnetes Werk:	Enthalten in: Applied energy - Amsterdam [u.a.] : Elsevier Science, 1975, 341
Übergeordnetes Werk:	volume:341

DOI / URN:	10.1016/j.apenergy.2023.121106

Katalog-ID:	ELV009693777

Internformat


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245	1	0	\|a Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
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520			\|a Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions.
650		4	\|a Model predictive control
650		4	\|a Prediction uncertainty
650		4	\|a HVAC system management
650		4	\|a Tube-based model predictive control
700	1		\|a Miyata, Shohei \|e verfasserin \|0 (orcid)0000-0002-0099-8857 \|4 aut
700	1		\|a Akashi, Yasunori \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Applied energy \|d Amsterdam [u.a.] : Elsevier Science, 1975 \|g 341 \|h Online-Ressource \|w (DE-627)320406709 \|w (DE-600)2000772-3 \|w (DE-576)256140251 \|x 1872-9118 \|7 nnns
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912			\|a GBV_ILN_105
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912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
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936	b	k	\|a 52.50 \|j Energietechnik: Allgemeines \|q VZ
951			\|a AR
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publishDate	2023
allfields	10.1016/j.apenergy.2023.121106 doi (DE-627)ELV009693777 (ELSEVIER)S0306-2619(23)00470-1 DE-627 ger DE-627 rda eng 620 VZ 52.50 bkl Gao, Yuan verfasserin (orcid)0000-0002-3526-5856 aut Energy saving and indoor temperature control for an office building using tube-based robust model predictive control 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions. Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control Miyata, Shohei verfasserin (orcid)0000-0002-0099-8857 aut Akashi, Yasunori verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 341 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:341 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.50 Energietechnik: Allgemeines VZ AR 341
spelling	10.1016/j.apenergy.2023.121106 doi (DE-627)ELV009693777 (ELSEVIER)S0306-2619(23)00470-1 DE-627 ger DE-627 rda eng 620 VZ 52.50 bkl Gao, Yuan verfasserin (orcid)0000-0002-3526-5856 aut Energy saving and indoor temperature control for an office building using tube-based robust model predictive control 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions. Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control Miyata, Shohei verfasserin (orcid)0000-0002-0099-8857 aut Akashi, Yasunori verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 341 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:341 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.50 Energietechnik: Allgemeines VZ AR 341
allfields_unstemmed	10.1016/j.apenergy.2023.121106 doi (DE-627)ELV009693777 (ELSEVIER)S0306-2619(23)00470-1 DE-627 ger DE-627 rda eng 620 VZ 52.50 bkl Gao, Yuan verfasserin (orcid)0000-0002-3526-5856 aut Energy saving and indoor temperature control for an office building using tube-based robust model predictive control 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions. Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control Miyata, Shohei verfasserin (orcid)0000-0002-0099-8857 aut Akashi, Yasunori verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 341 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:341 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.50 Energietechnik: Allgemeines VZ AR 341
allfieldsGer	10.1016/j.apenergy.2023.121106 doi (DE-627)ELV009693777 (ELSEVIER)S0306-2619(23)00470-1 DE-627 ger DE-627 rda eng 620 VZ 52.50 bkl Gao, Yuan verfasserin (orcid)0000-0002-3526-5856 aut Energy saving and indoor temperature control for an office building using tube-based robust model predictive control 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions. Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control Miyata, Shohei verfasserin (orcid)0000-0002-0099-8857 aut Akashi, Yasunori verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 341 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:341 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.50 Energietechnik: Allgemeines VZ AR 341
allfieldsSound	10.1016/j.apenergy.2023.121106 doi (DE-627)ELV009693777 (ELSEVIER)S0306-2619(23)00470-1 DE-627 ger DE-627 rda eng 620 VZ 52.50 bkl Gao, Yuan verfasserin (orcid)0000-0002-3526-5856 aut Energy saving and indoor temperature control for an office building using tube-based robust model predictive control 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions. Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control Miyata, Shohei verfasserin (orcid)0000-0002-0099-8857 aut Akashi, Yasunori verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 341 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:341 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.50 Energietechnik: Allgemeines VZ AR 341
language	English
source	Enthalten in Applied energy 341 volume:341
sourceStr	Enthalten in Applied energy 341 volume:341
format_phy_str_mv	Article
bklname	Energietechnik: Allgemeines
institution	findex.gbv.de
topic_facet	Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control
dewey-raw	620
isfreeaccess_bool	false
container_title	Applied energy
authorswithroles_txt_mv	Gao, Yuan @@aut@@ Miyata, Shohei @@aut@@ Akashi, Yasunori @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320406709
dewey-sort	3620
id	ELV009693777
language_de	englisch
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author	Gao, Yuan
spellingShingle	Gao, Yuan ddc 620 bkl 52.50 misc Model predictive control misc Prediction uncertainty misc HVAC system management misc Tube-based model predictive control Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
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topic_title	620 VZ 52.50 bkl Energy saving and indoor temperature control for an office building using tube-based robust model predictive control Model predictive control Prediction uncertainty HVAC system management Tube-based model predictive control
topic	ddc 620 bkl 52.50 misc Model predictive control misc Prediction uncertainty misc HVAC system management misc Tube-based model predictive control
topic_unstemmed	ddc 620 bkl 52.50 misc Model predictive control misc Prediction uncertainty misc HVAC system management misc Tube-based model predictive control
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title	Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
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title_full	Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
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author_browse	Gao, Yuan Miyata, Shohei Akashi, Yasunori
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title_sort	energy saving and indoor temperature control for an office building using tube-based robust model predictive control
title_auth	Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
abstract	Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions.
abstractGer	Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions.
abstract_unstemmed	Actively controlling a building’s heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions.
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title_short	Energy saving and indoor temperature control for an office building using tube-based robust model predictive control
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author2	Miyata, Shohei Akashi, Yasunori
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doi_str	10.1016/j.apenergy.2023.121106
up_date	2024-07-07T00:02:04.699Z
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Energy saving and indoor temperature control for an office building using tube-based robust model predictive control

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