Sustainable comfort in buildings
Riffelli Stefano, University of Urbino
We are an indoor generation, spending 90% of our time indoors. And yet, very often, we stay in badly lit, noisy and poorly ventilated environments — in other words, harmful to our health.
It is well known that a comfortable environment positively impacts health, work productivity and learning curve. That’s why nowadays exceeding the habitability threshold is not enough, and it is important to reach for functional comfort or — even better — psychological comfort in our indoor environments.
The word “comfort” has a number of nuances and meanings, depending on its context. It is commonly believed that, in order to obtain a good comfort level, it is necessary to consume more energy. This research concerns “Sustainable comfort in buildings”, and can be divided into three main steps:
- The first is aimed at detecting the core parameters that define the user’s comfort in indoor environments.
- The second analyses the suggested concept of a “global comfort index”, identifying a methodological proposal for smart buildings.
- The third concerns the identification of algorithms, or, even better, AI (artificial intelligence) algorithms to generate suggestions and/or to control actuators in smart homes or buildings.
Defining the user’s comfort in buildings
It is possible to identify four main categories that define a user’s comfort in buildings. These are thermal comfort, acoustic comfort, visual comfort, and indoor air quality. For each of these comfort categories, there are many indices and parameters, including:
- Thermal comfort: PMV/PPD (Predicted Mean Vote / Predicted Percentage of Dissatisfied); Adaptive comfort model; Bioclimatic charts and other indices
- Acoustic comfort: Noise level; Echo; Acoustic privacy
- Visual comfort: Amount of light; Uniformity of light; Quality of light in rendering colours; Prediction of the glare risk for occupants
- Indoor air quality (IAQ): Pollutant concentration (e.g. CO2); Perceived air quality; Ventilation air flow rates
The “global comfort index”
The concept of “global comfort index” can be used to quantify the Indoor Environmental Quality (IEQ). This index will be called GCI-SB (Global Comfort Index in Smart Building).
GCI-SB is is aimed at managing an index to quantify, in the most possible objective way, the mere comfort of a certain smart building. The first step is identifying the main indoor environment comforts. Then — using all four aforementioned categories — to identify, within each comfort category, the indices and/or measurable levels which are consolidated and popular within the scientific community.
The PMV/PPD model for thermal comfort was chosen to be the most popular and consolidated the thermal comfort indices within the scientific field. For acoustic comfort, CO2 level for IAQ and amount of light for visual comfort can be considered extremely simplistic, but allow for a more objective global index.
The third step is to adapt each index to the same value range, in order to make it mathematically comparable and obtain a homogenous numeric evaluation for each index category. During this step, special attention must be paid to:
- either correspondence or conversion of any value on logarithmic scales (e.g. dBA in acoustic comfort);
- value attribution, where a (positive) increase of the examined parameter corresponds to a (negative) increase of the comfort evaluation (e.g. CO2 concentration level for indoor air quality);
- reporting as “sufficient” comfort value the minimum threshold (or acceptable) value, related to Smart Building.
The last step is to utilise a mathematic model that includes all comparable input indices and results in a single final value, representing the global comfort index over a certain scale.
The activation (or not) of actuators
The aim of providing suggestions and the activation (or not) of actuators obviously depends on the building’s occupants’ habits.
The first phase is the “setup” phase, i.e. understanding the correlation between objective and subjective data. On the one hand, objective data can be collected by a measurer for the above-mentioned physical quantities (e.g. through an IEQ logger) that will collect data through sensors. On the other hand, subjective data can be obtained through immediate feedback or by answering simple questionnaires.
Both the data from the sensors and the answers from the questionnaires will be sent to a database.
The last phase is to identify machine learning, deep learning or, more in general, AI algorithms. These must acquire the GCI-SB, occupant’s feedback, and energy consumption analysis as input, providing suggestions and controlling actuators in their output.
The final goal is achieving high levels of comfort with low energy consumption, moving towards smart, green, and nearly Zero Energy Buildings (nZEB).
Find out more about Riffelli Stefano’s research and watch his presentation via the Climate Exp0 media library.
Climate Exp0 is the first virtual conference from the COP26 Universities Network and the Italian University Network for Sustainable Development (RUS), sponsored by UK Research and Innovation (UKRI), Cambridge University Press, the Conference of Italian University Rectors (CRUI), and the 2021 UN Climate Change Summit (COP26).
Running from 17–21 May 2021, it takes place at a critical juncture in the COP26 pre-meetings and negotiations, and is part of the All4Climate Italy 2021 official pre-COP26 initiatives. Learn more and register your place via https://www.climateexp0.org.
