Future Weather Building Simulation

Filters

CMIP3CMIP5CMIP6CORDEX

Selected filters

Studies: | Locations:

Selected study

Study title

Authors

Abstract:

Project:
Global climate model:
Regional climate model:
Forcing scenario:
Downscaling method:
Simulation engine:
Typology:
Location:

Author year	Location	Project	GCM	RCM	Scenario	Downscaling	Engine	DOI
Richter2023	Germany	CMIP6	EC-Earth3	-	SSP5-8.5	Future Weather Generator	EnergyPlus	https://doi.org/10.3390/app132212478
Arima2023	Japan	CMIP3, CMIP5	MIROC6h, MRI-ESM2-0	-	RCP 4.5, RCP 8.5, A2, A1B	CCWorldWeatherGen, WeatherShift™, Meteonorm 8, Extended AMeDAS (EA), NIES	THERB for HAM	https://doi.org/10.1051/e3sconf/202339605014
Escandón2023	Spain	HadCM3	HadCM3	-	RCP 8.5, A2	Meteonorm, CCWorldWeatherGen	EnergyPlus	https://doi.org/10.3390/buildings13092385
Rodrigues2023	Portugal	CMIP6	EC-Earth3	-	SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5	Morphing	EnergyPlus	https://doi.org/10.1016/j.buildenv.2023.110104
Sayadi2022	Sweden	CORDEX, CMIP5	-	REMO2015	RCP 8.5	RCM, statistical	IDA ICE	https://doi.org/10.1016/j.enbuild.2022.111960
DAgostino2022	Europe	CMIP5	14 different	-	RCP 8.5	WeatherShift™	EnergyPlus	https://doi.org/10.1016/j.energy.2021.122479
Ma2022	Australia	CMIP5	CSIRO	-	RCP 8.5	Morphing	EnergyPlus	https://doi.org/10.3390/buildings12081275
Martins2022	Iberian Peninsula	CMIP5	HadCM3	WRF	RCP 8.5	Morphing	EnergyPlus	https://doi.org/10.1016/j.jobe.2022.105598
Jafarpur2021	Canada	CMIP5	14 different	-	RCP 8.5	WeatherShift™	EnergyPlus	https://doi.org/10.1016/j.jobe.2021.102725
Zou2021	China	CMIP5	GISS-E2-R	-	RCP2.6, RCP4.5, RCP6.0, RCP8.5	Morphing	EnergyPlus	https://doi.org/10.1016/j.buildenv.2021.107663
Nguyena2021	Southeast Asia	CMIP5	14 different	-	RCP4.5, RCP8.5	WeatherShift™	EnergyPlus	https://doi.org/10.1016/j.jobe.2020.102089
Tsoka2021	Greece	CMIP5	HadGEM2	RegCM4	RCP4.5	Meteonorm	EnergyPlus	https://doi.org/10.3390/en14185799
Farahani2021	Finland	CMIP5	28 different	-	RCP 4.5	Statistical	IDA ICE	https://doi.org/10.3390/app11093972
Tootkaboni2021	Rome	CORDEX, CMIP5	MPI-ESM-LR	REMO2015	RCP8.5	CCWorldWeatherGen, RCM, WeatherShift, Meteonorm	EnergyPlus	https://doi.org/10.3390/cli9020037
Yang2021	Europe	CORDEX, CMIP5	5 different	RCA4	RCP2.6, RCP4.5, RCP8.5	RCM	Matlab/Simulink	https://doi.org/10.1088/1742-6596/2069/1/012069
Tootkaboni2021a	Italy	CMIP5	14 different	-	RCP4.5, RCP8.5	WeatherShift™	EnergyPlus	https://doi.org/10.1016/j.egyr.2021.04.012
BravoDias2020	Iberia	CMIP3, CMIP5	HadCM3	WRF	A2	CCWorldWeatherGen, Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2019.109556
Liu2020	Hong Kong	CMIP5	24 different	-	RCP2.6, RCP4.5, RCP6.0, RCP8.5	Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2019.109696
Hosseini2020	Montreal international airport	CMIP5	GFDL-ESM2	-	RCP2.6, RCP4.5, RCP6.0, RCP8.5	Machine Learning	EnergyPlus	https://doi.org/10.1016/j.enbuild.2020.110543
Machard2020	Paris	CORDEX, CMIP5	6 different	11 different GCM-RCM combinations	RCP4.5, RCP8.5	RCM	EnergyPlus	https://doi.org/10.3390/en13133424
Ciancio2020	Europe	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.scs.2020.102213
Rodriguez2020	Spain	CMIP3	HadCM3	-	A1F1, A2, B1, B2	Weather Morph	EnergyPlus	https://doi.org/10.3390/en13236188
Mangan2020	Turkey	CMIP3	-	-	A2	Meteonorm	EnergyPlus	https://doi.org/10.1080/23311916.2020.1714112
Picard2020	North America	CMIP3,CMIP5	1 and 14 different	-	A2, RCP4.5, RCP8.5	CCWorldWeatherGen, WeatherShift	EnergyPlus	https://doi.org/10.1016/j.enbuild.2020.110251
Rodrigues2020	Mediterranean	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.apenergy.2019.114110
Bamdad2020	Australia	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.enbuild.2020.110610
Farah2019	Australia	CMIP5	-	-	RCP4.5	Time series analysis	TRNSYS	https://doi.org/10.1016/j.enbuild.2018.11.045
Dino2019	Turkey	CMIP5	14 different	-	RCP8.5	WeatherShift	EnergyPlus	https://doi.org/10.1016/j.renene.2019.03.150
Rouault2019	Chile	CMIP5	MIROC-ESM,IPSL-CM5A,CCSM4	-	RCP4.5, RCP8.5	Meteonorm	ISO 13790:2008	https://doi.org/10.3390/su11247068
Nematchoua2019	India Ocean	CMIP3	18 different	-	A2, A1B, B1	Meteonorm	EnergyPlus	https://doi.org/10.1016/j.scs.2018.10.031
Moazami2019	Geneva	CORDEX, CMIP5, CMIP3	4 different	RCA4	A2, RCP4.5, RCP8.5	CCWorldWeatherGen, WeatherShift™, Meteonorm, RCM	EnergyPlus	https://doi.org/10.1016/j.apenergy.2019.01.085
Silvero2019	Paraguay	CORDEX, CMIP5	ECMWF-ERAINT, HadGEM2-ES	RCA4	RCP4.5, RCP8.5	RCM, Statistical	EnergyPlus	https://doi.org/10.1007/s12273-019-0532-6
Troup2019	USA	CMIP5	14 different	-	RCP4.5, RCP8.5	WeatherShift™	EnergyPlus	https://doi.org/10.1016/j.apenergy.2019.113821
Flores-Larsen2019	Argentina	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.enbuild.2018.12.015
Dodoo2019	Ghana	CMIP3	-	-	A1B	Meteonorm	IDA ICE	https://doi.org/10.3390/buildings9100215
Huang2019	Taiwan	CMIP5	NorESM1-M	-	RCP2.6, RCP4.5, RCP8.5	Morphing	EnergyPlus	https://doi.org/10.1051/e3sconf/201911106056
Zhai2019	USA	CMIP5	CCSM4, FIO-ESMv1.0, HadGEM2-ES	-	RCP2.6, RCP4.5, RCP6.0, RCP8.5	Morphing	EnergyPlus	https://doi.org/10.1016/j.scs.2018.10.043
Baniassadi2019	USA	CMIP5	CESM1	WRF	RCP 8.5	RCM	EnergyPlus	https://doi.org/10.1088/1748-9326/ab28ba
Pouriya2019	Toronto	CMIP3, CMIP5	HadCM3	HRM3	A2, RCP8.5	CCWorldWeatherGen, RCM, WeatherShift™	NaN	https://doi.org/10.1088/1757-899X/609/7/072037
Zheng2019	USA	CMIP3	HadCM3	-	A1F1, A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.energy.2019.04.052
Triana2018	Brazil	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.enbuild.2017.11.003
Cellura2018	Europe	CMIP5	24 different	-	RCP2.6, RCP4.5, RCP6.0, RCP8.5	Morphing	TRNSYS	https://doi.org/10.1016/j.esd.2018.05.001
Perez-Andreu2018	Valencia	CMIP5	CNRM-CM5, MPI-ESM-LR	-	RCP8.5	Morphing	TRNSYS	https://doi.org/10.1016/j.energy.2018.09.015
Hosseini2018	Montreal	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.jobe.2018.02.001
Suarez2018	Córdoba	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.3390/su10082914
Vasaturo2018	Netherlands	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1007/s12273-018-0470-8
Andric2017	Europe and Canada	CMIP3	HadCM3	-	low, med, high	CCWorldWeatherGen	Matlab/Simulink	https://doi.org/10.1016/j.enbuild.2017.05.047
Shen2017	USA	CMIP3	HadCM3	-	A1F1, A2	Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2016.09.028
Lim2017	Asia	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.3390/su9112039
Pierangioli2017	Firenze	CORDEX, CMIP5	-	COSMO-CLM	RCP8.5	Morphing	EnergyPlus	https://doi.org/10.1007/s12273-016-0346-8
Sabunas2017	Lithuania	CMIP5	5 different	-	RCP2.6, RCP8.5	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.egypro.2017.09.020
Filippin2017	Argentina	CMIP5	MRI-CGCM3	-	RCP4.5	-	SIMEDIF	https://doi.org/10.1016/j.renene.2016.09.064
Hamdy2017	Netherlands	CMIP3	5 different	8 different	KNMI'06 G+, KNMI'06 W+	Hybrid	IDA ICE	https://doi.org/10.1016/j.buildenv.2017.06.031
Palme2017	Ecuador	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	TRNSYS	https://doi.org/10.1007/978-3-319-30746-6_31
Wang2017	USA	CMIP5, CMIP3	CESM1(CAM5), HadCM3	-	RCP2.6, RCP4.5, RCP8.5, A2	CCWorldWeatherGen, Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2017.01.007
Erba2017	Milano	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.egypro.2017.09.561
Nik2017	Sweden	CMIP3	CNRM-CM3, ECHAM5	RCA3	A1B	RCM	Matlab/Simulink	https://doi.org/10.1016/j.egypro.2017.09.686
Huang2016	Taipei	CMIP3	MIROC3.2-M	-	A2, A1B, B1	Morphing	EnergyPlus	https://doi.org/10.1016/j.apenergy.2015.11.008
Waddicor2016	Turin city	CMIP3	-	-	A2, B1	Morphing	IDA ICE	https://doi.org/10.1016/j.buildenv.2016.03.003
Dodoo2016	Växjö	CMIP5	HadGEM2	-	RCP4.5, RCP8.5	Morphing	VIP-Energy	https://doi.org/10.1016/j.energy.2015.12.086
Pagliano2016	Italy	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.enbuild.2016.05.092
Huang2016	USA	CMIP3	15 different	-	A2, A1B, B1	Morphing	EnergyPlus	https://doi.org/10.1016/j.energy.2016.05.118
Nik2016	Sweden	CMIP3	5 different	RCA3	A1B3	RCM	Matlab/Simulink	https://doi.org/10.1016/j.enbuild.2016.03.044
Khalfan2016	Qatar	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	IESVE	https://doi.org/10.3390/su8020139
Arima2016	Tokyo	CMIP5	MIROC4h	WRF	RCP4.5	RCM	TRNSYS	https://doi.org/10.1016/j.enbuild.2015.08.019
Invidiata2016	Brasilian cities	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.enbuild.2016.07.067
Rubio-Bellido2016	Chilean cities	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	Excel VBA	https://doi.org/10.1016/j.energy.2016.08.021
Shibuya2016	Japan	CMIP3	MRI-CGCM2	RCM20	A2	RCM	TAS	https://doi.org/10.1016/j.enbuild.2016.02.023
Zhu2016	Shanghai	CMIP5	HadGEM2-CC	-	RCP4.5	Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2015.12.020
Karimpour2015	Adelaide	CMIP3	CSIRO-Mk3.0	-	A1B, B1	Morphing	AccuRate	https://doi.org/10.1016/j.enbuild.2014.10.064
Jylha2015	Finland	CMIP3	19 different	-	A2, A1B, B1	Morphing	IDA ICE	https://doi.org/10.1016/j.dib.2015.04.026
Kikumoto2015	Japan	CMIP5	MIROC4h	WRF	RCP4.5	RCM	TRNSYS	https://doi.org/10.1016/j.scs.2014.08.007
Wang2014	US cities	CMIP3	HadCM3	-	A1F1, A2, B1	Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2014.07.034
Berger2014	Wien	CMIP3	-	REMO-UBA	A1B	RCM	TAS	https://doi.org/10.1016/j.enbuild.2014.03.084
Dirks2015	USA	CMIP3	CASCaDe	-	A2	Statistical	EnergyPlus	https://doi.org/10.1016/j.energy.2014.08.081
Peng2014	Sheffield	CMIP3	HadCM3	-	A2	CCWeatherGen	EnergyPlus	https://doi.org/10.1109/IGBSG.2014.6835172
Daly2014	Australia	CMIP3	HadCM3	-	A2	CCWorldWeatherGen	EnergyPlus	https://doi.org/10.1016/j.buildenv.2014.01.008
Lee2013	England	CMIP3	HadCM3Q0	HadRM3Q0	A1B	RCM	EnergyPlus	https://doi.org/10.1177/0143624412439485
Xiang2013	China	CMIP3	MIROC3.2-H	-	A1B, B1	Statistical	TRNSYS	https://doi.org/10.1007/s11708-013-0261-y
Asimakopoulos2012	Greece	CMIP3	-	-	A2, B2, A1B	RCM	TRNSYS	https://doi.org/10.1016/j.enbuild.2012.02.043
Eames2012	England	CMIP3	HadCM3	-	low, med, high	Morphing	NaN	https://doi.org/10.1016/j.buildenv.2012.03.006
Ouedraogo2012	Burkina Faso	CMIP3	HadCM3	-	A1, A2, B1, B2	Statistical	IESVE	https://doi.org/10.1016/j.buildenv.2011.10.003
Wan2012	China	CMIP3	MIROC3.2-H	-	A1B, B1	-	DOE-2	https://doi.org/10.1016/j.apenergy.2011.11.048
Guan2012	Australia	CMIP3	-	-	low, med, high	Statistical	EnergyPlus	https://doi.org/10.1016/j.buildenv.2011.11.013
Chan2011	Hong Kong	CMIP3	6 different	-	A1B, B1	Morphing	EnergyPlus	https://doi.org/10.1016/j.enbuild.2011.07.003
Wang2010	Australia	CMIP3	CSIRO-Mk3.0	-	A1B, A1F1, A1T	Morphing	AccuRate	https://doi.org/10.1016/j.buildenv.2010.01.022
Jentsch2008	Southampton	CMIP3	UKCIP02	-	med, high	Morphing	TRNSYS	https://doi.org/10.1016/j.enbuild.2008.06.005
Crawley2007	USA	CMIP3	HadCM3	-	A1F1, A2, B1, B2	-	EnergyPlus	https://doi.org/10.1080/19401490802182079