The influence of calculation error of hourly marine meteorological parameter on building energy consumption calculation
Tài liệu tham khảo
Afrifa, 2020, Missing data imputation of high-resolution temporal climate time series data, Meteorol. Appl., 27, e1873, 10.1002/met.1873
Alexandru, 2012, Fitting data using optimal Hermite type cubic interpolating splines, Appl. Math. Lett., 25, 2047, 10.1016/j.aml.2012.04.016
Ali, 2018, Filling missing meteorological data in heating and cooling seasons separately, Int. J. Climatol., 1
Alvera, 2005, Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature, Ocean Model., 9, 325, 10.1016/j.ocemod.2004.08.001
Carbonell, 2016, Simulation of a solar-ice system for heating applications. system validation with one-year of monitoring data, Energy Build., 127, 846, 10.1016/j.enbuild.2016.06.058
Eldrandaly, 2011, Comparison of six GIS-based spatial interpolation methods for estimating air temperature in western Saudi Arabia, J. Environ. Inf., 18, 38, 10.3808/jei.201100197
Gebhardt, 2000, Optimal averaging of incomplete climatological data, Theor. Appl. Climatol., 65, 137, 10.1007/s007040070039
Han, 2021, Determination of HVAC meteorological parameters for floating nuclear power stations (FNPSs) in the area of China Sea and its vicinity, Energy, 121084, 10.1016/j.energy.2021.121084
Jain, 2017, Comparison of methods for spatial interpolation of fire weather in Alberta, Canada, Can. J. For. Res., 47, 1646, 10.1139/cjfr-2017-0101
Li, 2012, Building energy efficiency for sustainable development in China: challenges and opportunities, Build. Res. Inf., 40, 417, 10.1080/09613218.2012.682419
Liu, 2017, Sensitivity analysis of meteorological parameters on building energy consumption, Energy Proc., 132, 634, 10.1016/j.egypro.2017.09.700
Liu, 2017, Comparing micro-scale weather data to building energy consumption in Singapore, Energy Build., 152, 776, 10.1016/j.enbuild.2016.11.019
Lupato, 2019, Italian TRYs: new weather data impact on building energy simulations, Energy Build., 185, 287, 10.1016/j.enbuild.2018.12.001
Ma, 2020, Impact of meteorological factors on high-rise office building energy consumption in Hong Kong: from a spatiotemporal perspective, Energy Build., 228, 110468, 10.1016/j.enbuild.2020.110468
Moritz, 2015, ImputeTS: time series missing value imputation in R, R Journal, 9, 10.32614/RJ-2017-009
Nematchoua, 2019, Impact of climate change on demands for heating and cooling energy in hospitals: an in-depth case study of six islands located in the Indian Ocean region, Sustain. Cities Soc., 44, 629, 10.1016/j.scs.2018.10.031
Pieter, 2018, 3
Pincetl, 2016, Analysis of high-resolution utility data for understanding energy use in urban systems: the case of Los Angeles, California, J. Ind. Ecol., 20, 166, 10.1111/jiec.12299
Rocha, 2019, Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agroclimatic data, Comput. Electron. Agric., 157, 427, 10.1016/j.compag.2019.01.018
Tsoka, 2017, Evaluation of stochastically generated weather datasets for building energy simulation, Energy Proc., 122, 853, 10.1016/j.egypro.2017.07.449
Van, 2011, MICE: multivariate imputation by chained equations in R, J. Stat. Software, 45, 1
Wang, 2009, Marine meteorology research progress of China from 2003 to 2006, Adv. Atmos. Sci., 26, 17, 10.1007/s00376-009-0017-0
Yodah, 2013, Imputation of incomplete non-stationary seasonal time series data, Math. Theor. Model., 3, 142
Yozgatligil, 2013, Comparison of missing value imputation methods in time series: the case of Turkish meteorological data, Theor. Appl. Climatol., 112, 143, 10.1007/s00704-012-0723-x
