The analysis of the sustainability of buildings involves a holistic view that includes the entire life cycle of the building (production, use, decommissioning) according to the Life Cycle Analysis (LCA) approach.
The software tools used for this kind of analysis typically make use of eco-labels of building materials and products available on the market, according to the International Standard of the Environmental Product Declaration (EPD), with the possibility of considering alternative scenarios for the recovery and reuse of waste materials that are specific to the site where the building is located.
Multi-criteria and analytical scoring tools are also used, typical of the most common environmental certification schemes such as LEED (https://www.usgbc.org/leed), BREEAM (https://kb.breeam.com/) and WELL, capable of assigning a score according to sustainability criteria such as the consumption of embodied energy, drinking water and land use.
The most commonly used tools to simulate environmental comfort and calculate the most relevant comfort indicators are listed and described at the following link:
http://www.ibpsa-italy.org/en/software-en/sustainability.html
When dealing with environmental comfort, building simulations are useful to predict the time trends of dry bulb temperature, relative humidity, air velocity and possibly pollutant concentration in indoor and outdoor spaces. In this case, building simulations are frequently performed with the same software tools that are used to estimate the energy performance, but they are conducted under “free running” conditions, i.e. without considering any mechanical systems able to control the above parameters.
Then, the results of the building simulations must be post-processed through suitable tools that can assess a series of comfort parameters and indicators, according to International Standards that integrate well-known comfort theories, such as ANSI ASHRAE Standard 55-2020 and EN Standard 16798-1:2019.
The most commonly used tools to simulate environmental comfort and calculate the most relevant comfort indicators are listed and described at the following link:
http://www.ibpsa-italy.org/en/software-en/environmental-comfort-en.html
In the field of lighting, simulations can be used to evaluate different aspects related either to electric light or natural light. Depending on your specific goal, you need to choose the most appropriate software.
A first distinction must be made between software based on a static calculation approach and those based on a dynamic approach. Thanks to the former, it is possible to evaluate the distribution of electric or natural light in the environment; calculate the parameters for which the standard provides the limit values in order to meet the conditions of visual comfort in the presence of electric light (such as the values of illuminance and uniformity on the surfaces of the space or the values of UGR); Evaluate the availability of natural light in specific weather conditions (e.g. clear or overcast sky) and at specific times and days of the year.
Thanks to dynamic calculation software, on the other hand, it is possible to evaluate the availability of natural light in indoor environments over the course of an entire year and with a generally hourly interval, considering the variations in the specific climatic conditions of a given geographical area, modeled from the data contained in the climate files. In this way, it is possible to obtain more complete information on the distribution of natural light in the environment during the year, to evaluate the amount of electric light necessary to integrate natural light over time, to calculate the corresponding energy consumption, to analyze the performance of automatic control systems or shading systems and to evaluate, with a statistical approach, the occurrence of any risks of uncomfortable conditions due to the presence of natural light.
Finally, it should be emphasized that researchers are paying great attention to the so-called “non-visual effects” of lighting (effects on mood, work performance, circadian rhythms), which has led to the development of some software capable of accurately simulating the spectral interactions between light and matter and then evaluating the spectral irradiance to the eye of the occupants of a space, thanks to which it is then possible to estimate the non-visual effects of light.
The most common simulation tools in the field of lighting are listed and described below:
http://www.ibpsa-italy.org/en/software-en/lighting.html
Combined Heat, Air and Moisture Transfer (HAMT) simulations are useful when one needs to investigate the transient hygrothermal performance of buildings and building components, with a special focus on moisture transfer and storage both in vapor and liquid form. This allows, for instance, to predict possible moisture-related damage either in wood-based materials (or other organic moisture-sensitive ones) or in historical buildings. HAMT simulations can be also used to quantify heat losses in thermal bridges.
HAMT simulations tools numerically solve combined heat and mass balances in 1D or 2D geometries over medium-to-long time spans, and require the availability of reliable updated weather data. The results (in terms of temperature, relative humidity, and moisture content in the various wall layers) can be post-processed to assess a series of risk indicators widely available in the literature, while also investigating the effectiveness of possible mitigation strategies, such as waterproof and vapour-open membranes.
The most commonly HAMT simulation tools are listed and described at the following link:
http://www.ibpsa-italy.org/en/software-en/heat-and-mass-transfer.html
The building energy performance concerns the energy required for the use of the building. It can include – as expressed by the EPBD recast for a standard use – the energy used for space heating, space cooling, ventilation, domestic hot water production, electric lighting.
The energy performance can be expressed by means of indicators that quantify, in function of the aim and the field of application, the useful energy need, the primary energy use, the energy delivered to the building by energy service and energy carrier, the energy produced by renewable energy sources, etc.
The energy performance modelling of buildings is a topic still deeply investigated by the research community. From literature analysis, the available methods for the energy performance assessment are classified in simplified methods (e.g., those derived from technical standards), simulation models (e.g., detailed dynamic numerical simulation tools), statistical methods (e.g., regression models), machine learning techniques (e.g., neural networks), and other methodologies.
The most common simulation tools for the energy performance assessment are listed and described at the following link:
http://www.ibpsa-italy.org/en/software-en/energy-performance.html