TY - JOUR
T1 - Hourly simulation results of building energy simulation tools using a reference office building as a case study
AU - Magni, Mara
AU - Ochs, Fabian
AU - de Vries, Samuel
AU - Maccarini, Alessandro
AU - Sigg, Ferdinand
N1 - © 2021 The Authors.
PY - 2021
Y1 - 2021
N2 - The data presented in this article are the results of widespread building simulation tools (i.e. EnergyPlus, TRNSYS, Simulink/CarnotUIBK, Simulink/ALMABuild, IDA ICE, Modelica/Dymola and DALEC) used to simulate a characteristic office cell, described within IEA SHC Task 56 [1], located in Stockholm, Stuttgart and Rome. Hourly data for each component of the thermal balance (i.e. Heating, cooling, infiltration, ventilation, internal gains, solar gains) and the hourly convective and radiative temperatures are reported for all the tools along with the ambient temperature and solar irradiation on the south façade. The mainly used statistical indices (i.e. Mean Bias Error, Mean Absolute Error, Root Mean Square Error and coefficient of determination) are applied to evaluate the accuracy of the tools. For more insight and interpretation of the results, please see “Detailed Cross Comparison of Building Energy Simulation Tools Results using a reference office building as a case study” [2]. This data set and evaluation methods are made available to ease the cross-validation process for other researchers.
AB - The data presented in this article are the results of widespread building simulation tools (i.e. EnergyPlus, TRNSYS, Simulink/CarnotUIBK, Simulink/ALMABuild, IDA ICE, Modelica/Dymola and DALEC) used to simulate a characteristic office cell, described within IEA SHC Task 56 [1], located in Stockholm, Stuttgart and Rome. Hourly data for each component of the thermal balance (i.e. Heating, cooling, infiltration, ventilation, internal gains, solar gains) and the hourly convective and radiative temperatures are reported for all the tools along with the ambient temperature and solar irradiation on the south façade. The mainly used statistical indices (i.e. Mean Bias Error, Mean Absolute Error, Root Mean Square Error and coefficient of determination) are applied to evaluate the accuracy of the tools. For more insight and interpretation of the results, please see “Detailed Cross Comparison of Building Energy Simulation Tools Results using a reference office building as a case study” [2]. This data set and evaluation methods are made available to ease the cross-validation process for other researchers.
KW - Building simulation
KW - Cross comparison
KW - Statistical indices
KW - Building simulation
KW - Cross comparison
KW - Statistical indices
UR - http://www.scopus.com/inward/record.url?scp=85115288992&partnerID=8YFLogxK
U2 - 10.1016/j.dib.2021.107370
DO - 10.1016/j.dib.2021.107370
M3 - Journal article
C2 - 34589559
SN - 2352-3409
VL - 38
JO - Data in Brief
JF - Data in Brief
M1 - 107370
ER -