Development of a Modelica-based simplified building model for district energy simulations

none4siUrban Building Energy Simulation (UBES) is an efficient tool to investigate and subsequently reduce energy demand of urban areas. Nevertheless, UBES has always been a challenging task due the trade-off between accuracy, computational speed and parametrization. In order to reduce these computation and parameterization requirements, model reduction and simplification methods aim at representing building behaviour with an acceptable accuracy, but using less equations and input parameters. This paper presents the development and validation results of a simplified urban simulation model based on the ISO 13790 Standard and written in the Modelica language. The model describes the thermo-physical behaviour of buildings by means of an equivalent electric network consisting of five resistances and one capacitance. The validation of the model was carried out using four cases of the ANSI/ASHRAE Standard 140. In general, the model shows good accuracy and the validation provided values within the acceptable ranges. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.noneMaccarini, Alessandro; Prataviera, Enrico; Zarrella, Angelo; Afshari, AlirezaMaccarini, Alessandro; Prataviera, Enrico; Zarrella, Angelo; Afshari, Alirez

Hourly simulation results of building energy simulation tools using a reference office building as a case study

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