Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model

Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a lon...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fang, Zhen [verfasserIn] Crimier, Nicolas [verfasserIn] Scanu, Lisa [verfasserIn] Midelet, Alphanie [verfasserIn] Alyafi, Amr [verfasserIn] Delinchant, Benoit [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI)

Übergeordnetes Werk:	Enthalten in: Energy and buildings - Amsterdam [u.a.] : Elsevier Science, 1977, 245
Übergeordnetes Werk:	volume:245

DOI / URN:	10.1016/j.enbuild.2021.111053

Katalog-ID:	ELV00614053X

Internformat


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245	1	0	\|a Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
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520			\|a Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique.
650		4	\|a Indoor temperature forecasting
650		4	\|a Smart building
650		4	\|a Energy saving
650		4	\|a HVAC
650		4	\|a Recurrent neural network
650		4	\|a LSTM
650		4	\|a Seq2seq model
650		4	\|a Multi-step forecasting
650		4	\|a Prediction interval (PI)
700	1		\|a Crimier, Nicolas \|e verfasserin \|4 aut
700	1		\|a Scanu, Lisa \|e verfasserin \|4 aut
700	1		\|a Midelet, Alphanie \|e verfasserin \|4 aut
700	1		\|a Alyafi, Amr \|e verfasserin \|4 aut
700	1		\|a Delinchant, Benoit \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Energy and buildings \|d Amsterdam [u.a.] : Elsevier Science, 1977 \|g 245 \|h Online-Ressource \|w (DE-627)308448030 \|w (DE-600)1502295-X \|w (DE-576)094752532 \|7 nnns
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912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
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936	b	k	\|a 56.65 \|j Bauökologie \|j Baubiologie
951			\|a AR
952			\|d 245

Indexfelder

author_variant	z f zf n c nc l s ls a m am a a aa b d bd
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publishDate	2021
allfields	10.1016/j.enbuild.2021.111053 doi (DE-627)ELV00614053X (ELSEVIER)S0378-7788(21)00337-6 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Fang, Zhen verfasserin aut Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique. Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI) Crimier, Nicolas verfasserin aut Scanu, Lisa verfasserin aut Midelet, Alphanie verfasserin aut Alyafi, Amr verfasserin aut Delinchant, Benoit verfasserin aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 245 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:245 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 245
spelling	10.1016/j.enbuild.2021.111053 doi (DE-627)ELV00614053X (ELSEVIER)S0378-7788(21)00337-6 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Fang, Zhen verfasserin aut Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique. Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI) Crimier, Nicolas verfasserin aut Scanu, Lisa verfasserin aut Midelet, Alphanie verfasserin aut Alyafi, Amr verfasserin aut Delinchant, Benoit verfasserin aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 245 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:245 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 245
allfields_unstemmed	10.1016/j.enbuild.2021.111053 doi (DE-627)ELV00614053X (ELSEVIER)S0378-7788(21)00337-6 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Fang, Zhen verfasserin aut Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique. Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI) Crimier, Nicolas verfasserin aut Scanu, Lisa verfasserin aut Midelet, Alphanie verfasserin aut Alyafi, Amr verfasserin aut Delinchant, Benoit verfasserin aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 245 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:245 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 245
allfieldsGer	10.1016/j.enbuild.2021.111053 doi (DE-627)ELV00614053X (ELSEVIER)S0378-7788(21)00337-6 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Fang, Zhen verfasserin aut Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique. Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI) Crimier, Nicolas verfasserin aut Scanu, Lisa verfasserin aut Midelet, Alphanie verfasserin aut Alyafi, Amr verfasserin aut Delinchant, Benoit verfasserin aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 245 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:245 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 245
allfieldsSound	10.1016/j.enbuild.2021.111053 doi (DE-627)ELV00614053X (ELSEVIER)S0378-7788(21)00337-6 DE-627 ger DE-627 rda eng 690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Fang, Zhen verfasserin aut Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique. Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI) Crimier, Nicolas verfasserin aut Scanu, Lisa verfasserin aut Midelet, Alphanie verfasserin aut Alyafi, Amr verfasserin aut Delinchant, Benoit verfasserin aut Enthalten in Energy and buildings Amsterdam [u.a.] : Elsevier Science, 1977 245 Online-Ressource (DE-627)308448030 (DE-600)1502295-X (DE-576)094752532 nnns volume:245 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 52.42 Heizungstechnik Lüftungstechnik Klimatechnik 56.50 Technischer Ausbau 56.55 Bauphysik Bautenschutz 56.65 Bauökologie Baubiologie AR 245
language	English
source	Enthalten in Energy and buildings 245 volume:245
sourceStr	Enthalten in Energy and buildings 245 volume:245
format_phy_str_mv	Article
bklname	Heizungstechnik Lüftungstechnik Klimatechnik Technischer Ausbau Bauphysik Bautenschutz Bauökologie Baubiologie
institution	findex.gbv.de
topic_facet	Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI)
dewey-raw	690
isfreeaccess_bool	false
container_title	Energy and buildings
authorswithroles_txt_mv	Fang, Zhen @@aut@@ Crimier, Nicolas @@aut@@ Scanu, Lisa @@aut@@ Midelet, Alphanie @@aut@@ Alyafi, Amr @@aut@@ Delinchant, Benoit @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	308448030
dewey-sort	3690
id	ELV00614053X
language_de	englisch
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author	Fang, Zhen
spellingShingle	Fang, Zhen ddc 690 bkl 52.42 bkl 56.50 bkl 56.55 bkl 56.65 misc Indoor temperature forecasting misc Smart building misc Energy saving misc HVAC misc Recurrent neural network misc LSTM misc Seq2seq model misc Multi-step forecasting misc Prediction interval (PI) Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
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topic_title	690 DE-600 52.42 bkl 56.50 bkl 56.55 bkl 56.65 bkl Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model Indoor temperature forecasting Smart building Energy saving HVAC Recurrent neural network LSTM Seq2seq model Multi-step forecasting Prediction interval (PI)
topic	ddc 690 bkl 52.42 bkl 56.50 bkl 56.55 bkl 56.65 misc Indoor temperature forecasting misc Smart building misc Energy saving misc HVAC misc Recurrent neural network misc LSTM misc Seq2seq model misc Multi-step forecasting misc Prediction interval (PI)
topic_unstemmed	ddc 690 bkl 52.42 bkl 56.50 bkl 56.55 bkl 56.65 misc Indoor temperature forecasting misc Smart building misc Energy saving misc HVAC misc Recurrent neural network misc LSTM misc Seq2seq model misc Multi-step forecasting misc Prediction interval (PI)
topic_browse	ddc 690 bkl 52.42 bkl 56.50 bkl 56.55 bkl 56.65 misc Indoor temperature forecasting misc Smart building misc Energy saving misc HVAC misc Recurrent neural network misc LSTM misc Seq2seq model misc Multi-step forecasting misc Prediction interval (PI)
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title	Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
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title_full	Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
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author_browse	Fang, Zhen Crimier, Nicolas Scanu, Lisa Midelet, Alphanie Alyafi, Amr Delinchant, Benoit
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title_sort	multi-zone indoor temperature prediction with lstm-based sequence to sequence model
title_auth	Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
abstract	Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique.
abstractGer	Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique.
abstract_unstemmed	Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising the occupant comfort level, by providing more accurate control of the HVAC (heating, ventilating, and air conditioning) system. In order to make the best use of different input variables, a long short-term memory (LSTM) based sequence to sequence (seq2seq) model was proposed to make multi-step ahead forecasting. The out-of-sample forecasting capacity of the model was evaluated with regard to different forecast horizons by various evaluation metrics. A tailor-made metric was proposed to take account of the small daily-variation characteristic of indoor temperature. The model was benchmarked against Prophet and a seasonal naive model, showing that the current model is much more skillful and reliable in very short-term forecasting. A cross-series learning strategy was adopted to enable multi-zone indoor temperature forecasting with a more generalised model. Furthermore, the uncertainty in model parameters was quantified by prediction intervals created by Monte-Carlo dropout (MC-dropout) technique.
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title_short	Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
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author2	Crimier, Nicolas Scanu, Lisa Midelet, Alphanie Alyafi, Amr Delinchant, Benoit
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up_date	2024-07-06T20:21:46.105Z
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Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model

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