Building systems and indoor environment : simulation for design decision support

This paper outlines the state-of-the-art in integrated building simulation for design support. The ESP-r system is used as an example where integrated simulation is a core philosophy behind the development. The paper finishes with indicating a number of barriers, which hinder routine application of simulation for building design

Integrated simulation for (sustainable) building design : state-of-the-art illustration

Many buildings are still constructed or remodelled without consideration of energy conserving strategies or other sustainability aspects. To provide substantial improvements in energy consumption and comfort levels, there is a need to treat buildings as complete optimised entities not as the sum of a number of separately optimised components

Weather data around the world for design of field hospital HVAC

Field hospital (FH) is a military mobile complex to be deployed in almost any climate around the world. Heating, ventilation and air-conditioning (HVAC) system for the Czech Republic FH units is being redesigned. Computer simulation software will be used for the design of HVAC under variety of specific outdoor conditions. Simulation software requires weather data to calculate energy balance of buildings and HVAC systems. Currently there are several weather data sets available for this purpose. All contain weather data but they may differ significantly. Therefore they should be carefully selected prior to their use. Even though a lot of databases are available, there is poor access to proper data outside of U.S., Western Europe and Japan, and in non-typical regions in terms of simulation exercises (i.e. developing countries). This paper reviews accessible weather data files and suggests which weather data should be used for design and long-term performance analysis of FH HVAC system in various non-typical geographical locations around the world

Development Of Sets Of Simplified Building Models For Building Simulation

All research studies based either on analytical and numerical simulations or on testing facilities under controlled or monitored external conditions require some simplifications with respect to the real physical phenomena. The simplification level has to be accurately defined since it introduces some boundaries to the achievable results and to the possibility to generalize the research findings. In particular, this last aspect is very important: once the model complexity is chosen, the desired number of cases can be determined. As a rule, the more complex a model, the larger the sample required to get statistically significant results. In building simulations, we can distinguish two kinds of analyses: the studies aimed to analyze the building performance (e.g., the kind of glazing with the best energy performance in a certain climate, the optimum insulation thickness, the most convenient refurbishment approach) and those focused on building modelling, methods and assumptions (e.g., weather inputs, solar radiation and sky luminance models or algorithms for the calculation of heat transfer through the opaque envelope). As concerns the first group, when the focus is on existing buildings, the sample has to be representative of the building stock possible variability – especially when reference buildings are not available or their characteristics are not completely suitable for inferential statistics. For new building solutions or technologies, instead, proper samples are needed for their evaluation. In the second case, as well, proper building configurations have to be determined and they strongly depend on the researchers’ aims: for instance, the set for the assessment of energy balance models can be different from those for the evaluation of thermal-hygrometric or visual comfort models. In this work, we propose a method to manage the complexity of variables involved in building simulation studies and to identify groups of simplified building models – “shoe-box building models” – or domains of relevant variables suitable to have statistically significant results. The method is applied as an example to the definition of an appropriate set of configurations for the comparison of TRNSYS and EnergyPlus and to the analysis of the discrepancies of six output quantities, i.e., monthly energy needs, hourly peak loads and time of occurrences of hourly peak loads – both heating and cooling. A set of candidate variables describing the building envelope characteristics are studied for each comparison and those more significant are selected for further analyses by means of statistical screening methods (specifically, Spearman’s rank correlation coefficient). Six different short lists of significant buildings variables are then defined for detailed and extensive statistical studies with a full factorial analysis (or equivalent approaches): while in the preliminary study attention is paid in particular to the main effects with a monthly time-discretization of the outputs, the detailed analyses aim at investigating the interactions between the different variables, with shorter time-discretization and focus on the most interesting quantities with respect to the research targets