Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation

Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its signi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, Muxing [verfasserIn] Zhang, Xiaosong [verfasserIn] Guo, Siyi [verfasserIn] Xu, Xiaodong [verfasserIn] Chen, Jiayu [verfasserIn] Wang, Wei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation

Übergeordnetes Werk:	Enthalten in: Sustainable cities and society - Amsterdam [u.a.] : Elsevier, 2011, 74
Übergeordnetes Werk:	volume:74

DOI / URN:	10.1016/j.scs.2021.103227

Katalog-ID:	ELV006617085

Internformat


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100	1		\|a Zhang, Muxing \|e verfasserin \|4 aut
245	1	0	\|a Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
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520			\|a Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating.
650		4	\|a Urban micro-climate
650		4	\|a Long short-term memory
650		4	\|a Meteorological prediction
650		4	\|a Building energy estimation
700	1		\|a Zhang, Xiaosong \|e verfasserin \|4 aut
700	1		\|a Guo, Siyi \|e verfasserin \|4 aut
700	1		\|a Xu, Xiaodong \|e verfasserin \|4 aut
700	1		\|a Chen, Jiayu \|e verfasserin \|4 aut
700	1		\|a Wang, Wei \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Sustainable cities and society \|d Amsterdam [u.a.] : Elsevier, 2011 \|g 74 \|h Online-Ressource \|w (DE-627)635602792 \|w (DE-600)2573417-9 \|w (DE-576)336956703 \|7 nnns
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Indexfelder

author_variant	m z mz x z xz s g sg x x xx j c jc w w ww
matchkey_str	zhangmuxingzhangxiaosongguosiyixuxiaodon:2021----:ramcolmtpeitotruhoghrtrmmrntokihogemoioigo
hierarchy_sort_str	2021
publishDate	2021
allfields	10.1016/j.scs.2021.103227 doi (DE-627)ELV006617085 (ELSEVIER)S2210-6707(21)00505-9 DE-627 ger DE-627 rda eng 690 720 DE-600 Zhang, Muxing verfasserin aut Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating. Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation Zhang, Xiaosong verfasserin aut Guo, Siyi verfasserin aut Xu, Xiaodong verfasserin aut Chen, Jiayu verfasserin aut Wang, Wei verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 74 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:74 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_2006 GBV_ILN_2008 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_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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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 AR 74
spelling	10.1016/j.scs.2021.103227 doi (DE-627)ELV006617085 (ELSEVIER)S2210-6707(21)00505-9 DE-627 ger DE-627 rda eng 690 720 DE-600 Zhang, Muxing verfasserin aut Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating. Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation Zhang, Xiaosong verfasserin aut Guo, Siyi verfasserin aut Xu, Xiaodong verfasserin aut Chen, Jiayu verfasserin aut Wang, Wei verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 74 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:74 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_2006 GBV_ILN_2008 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_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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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 AR 74
allfields_unstemmed	10.1016/j.scs.2021.103227 doi (DE-627)ELV006617085 (ELSEVIER)S2210-6707(21)00505-9 DE-627 ger DE-627 rda eng 690 720 DE-600 Zhang, Muxing verfasserin aut Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating. Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation Zhang, Xiaosong verfasserin aut Guo, Siyi verfasserin aut Xu, Xiaodong verfasserin aut Chen, Jiayu verfasserin aut Wang, Wei verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 74 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:74 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_2006 GBV_ILN_2008 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_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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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 AR 74
allfieldsGer	10.1016/j.scs.2021.103227 doi (DE-627)ELV006617085 (ELSEVIER)S2210-6707(21)00505-9 DE-627 ger DE-627 rda eng 690 720 DE-600 Zhang, Muxing verfasserin aut Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating. Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation Zhang, Xiaosong verfasserin aut Guo, Siyi verfasserin aut Xu, Xiaodong verfasserin aut Chen, Jiayu verfasserin aut Wang, Wei verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 74 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:74 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_2006 GBV_ILN_2008 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_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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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 AR 74
allfieldsSound	10.1016/j.scs.2021.103227 doi (DE-627)ELV006617085 (ELSEVIER)S2210-6707(21)00505-9 DE-627 ger DE-627 rda eng 690 720 DE-600 Zhang, Muxing verfasserin aut Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating. Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation Zhang, Xiaosong verfasserin aut Guo, Siyi verfasserin aut Xu, Xiaodong verfasserin aut Chen, Jiayu verfasserin aut Wang, Wei verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 74 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:74 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_2006 GBV_ILN_2008 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_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_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 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 AR 74
language	English
source	Enthalten in Sustainable cities and society 74 volume:74
sourceStr	Enthalten in Sustainable cities and society 74 volume:74
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation
dewey-raw	690
isfreeaccess_bool	false
container_title	Sustainable cities and society
authorswithroles_txt_mv	Zhang, Muxing @@aut@@ Zhang, Xiaosong @@aut@@ Guo, Siyi @@aut@@ Xu, Xiaodong @@aut@@ Chen, Jiayu @@aut@@ Wang, Wei @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	635602792
dewey-sort	3690
id	ELV006617085
language_de	englisch
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author	Zhang, Muxing
spellingShingle	Zhang, Muxing ddc 690 misc Urban micro-climate misc Long short-term memory misc Meteorological prediction misc Building energy estimation Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
authorStr	Zhang, Muxing
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topic_title	690 720 DE-600 Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation Urban micro-climate Long short-term memory Meteorological prediction Building energy estimation
topic	ddc 690 misc Urban micro-climate misc Long short-term memory misc Meteorological prediction misc Building energy estimation
topic_unstemmed	ddc 690 misc Urban micro-climate misc Long short-term memory misc Meteorological prediction misc Building energy estimation
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title	Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
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title_full	Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
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author_browse	Zhang, Muxing Zhang, Xiaosong Guo, Siyi Xu, Xiaodong Chen, Jiayu Wang, Wei
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author-letter	Zhang, Muxing
doi_str_mv	10.1016/j.scs.2021.103227
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title_sort	urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
title_auth	Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
abstract	Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating.
abstractGer	Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating.
abstract_unstemmed	Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating.
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title_short	Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation
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author2	Zhang, Xiaosong Guo, Siyi Xu, Xiaodong Chen, Jiayu Wang, Wei
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doi_str	10.1016/j.scs.2021.103227
up_date	2024-07-06T21:59:16.068Z
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Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation

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