Annex 86 Subtasks

Subtask 1 Metrics and development of an IAQ management strategy rating method

Subtask Leader: UK (U of Notthingham, Benjamin Jones) 
Co-Lead: Denmark (DTU, Pawel Wargocki)

This subtask is devoted to the development of a general rating method for the benchmarking of the performance of IAQ management systems. In addition to relevant metrics, a set of appropriate tools, consistent modeling assumptions and monitoring protocols are also proposed.


A1.1 Selecting performance metrics.

Starting from a thorough analysis of the metrics currently used in different partner countries, those described in literature dedicated to smart ventilation and in Annex 68, this activity addresses how to include, in addition to health, other factors such as comfort, productivity, user friendliness, durability, sustainability, resilience and economic factors (investment and operational costs) in a residential context. This includes proposing a method to aggregate the selected metrics for optimization purposes.

A1.2 Setting up a rating method.

The rating method describes the requirements for the modeling tools, the matching of relevant modeling assumptions and monitoring protocols. Rather than selecting 1 specific option for each of these, this activity is focused on systematically analyzing the properties and mechanisms of the IAQ management strategies under consideration and matching these with the available options.

A1.3 Use case testing.

This activity consists of checking that the needs of the stakeholders are met with the proposed rating method through use cases where the proposed methods are applied to current questions of the stakeholders.


D1.1 A literature review on relevant performance metrics.

D1.2 A literature review and synthesis report on appropriate modeling tools, relevant modeling assumptions and monitoring protocols

D1.3 A report of use cases of coherent sets of performance metrics and matching tools

Partners involved:

UoN (UK), PUC (Chile), BBRI (BE), UGent (BE), UoS (UK), ULR (FR), LU (UK), DTU (DK) , AAU (DK)

Stakeholders involved:

  • Policy makers and regulatory bodies will be able to use this method for the rating of energy efficient iaq management systems
  • Manufacturers (eg. enVerid (USA)) will be able to use the method to further develop and benchmark their product portfolio


Subtask 2 Source characterization and typical exposure in residential buildings

Subtask Leader: Austria (UASoS Salzburg, Gabriel Rojas) 
Co-Lead: France (ULR, Marc Abadie)

This ST creates consistent input values for the assessment method developed in ST 1 and control strategies in ST 4. It starts from information available in literature, adding new experimental results where needed and reviewing and developing models (empirical, semi-empirical or physical models) for characterizing relevant residential sources.

Areas of particular interest are:

  • Sources from building materials and pieces of furniture (review and consolidation of models and parameters)
  • Indoor sources from occupants and occupant activities (cooking, cosmetics, cleaning…) and occupancy patterns (partly drawing on the outputs of Annex 79)
  • Heating and cooking appliances (wood and gas stoves, fireplaces…)
  • Outdoor sources (from air and from ground)
  • Biological activity (fungal growth)
  • Secondary emissions due to indoor chemistry (e.g., O3-initiated reactions)
  • Bedroom environments
  • Epidemiology aspects (airborne transmission paths)


A2.1 Literature review

This activity includes a review of existing data regarding indoor sources of residential buildings and inputs regarding the outdoor sources. Emission rates of indoor sources will be compiled into an internet-based database available for the scientific community in open access.

A2.2 Monitoring and measurement campaigns

This activity will analyse data from planned, ongoing or past measurement campaigns to extract to provide data for source characterisation (ST2.3)

A2.3 Reviewing, Testing, Developing models for source characterization

This activity will review existing models for describing relevant pollutant sources, test, further develop and parametrize these models with available data (2.2). These models and model parameters will be documented and compiled in an open-access internet-based database.

Partners involved:

UASoS (AT), ULR (FR), ULille (FR), LU (UK), NUIG (Ire), UoS (UK), UoN (UK), EPFL (CH), SU (USA), NJU (CN), TU (CN), BBRI (BE), CETIAT (FR), PUC (Chile), BRANZ (NZ), AAU (DK)

Stakeholders involved:

Regulatory bodies, environmental health professionals and researchers will be able to refer to the collected input data


D2.1 Report on source characterization and typical exposure in residential buildings.

D2.2 An internet-based open access database of emission rates of indoor sources.

D2.3 A database on residential IAQ.


Subtask 3 Smart materials as an IAQ management strategy

Subtask Leader: Denmark (DTU, Menghao Qin)  
Co-Lead: USA (SU, Jensen Zhang)

This ST identifies opportunities to use the building structure and (bio-based) building materials (focussing on hemp concrete) and the novel functional materials inside it to actively/passively manage the IAQ, for example, through active paint, wallboards, textiles coated with advanced sorbents or hemp concrete, and quantifies their potential based on the assessment framework developed in ST 1.


A3.1 Material properties and characterization of the products

Literature survey and laboratory testing to gather relevant data and existing knowledge about properties for transport, retention and emission of chemical substances and moisture in new functional materials (eg. Metal-organic frameworks (MOFs), precise humidity control material (PHCM), hemp concrete, etc.). A starting point will be the further study of the similarities between VOC and moisture diffusion.

A3.2 Modeling of the behaviour under typical residential conditions

Model setup and laboratory tests to analyze the performance of the new materials for IAQ control in residential buildings. The behaviour of the materials over time under different climates will be analyzed and corresponding control strategies for IAQ management will be developed.

A3.3 Assessing energy saving and exposure reduction potential

Numerical simulations to study the energy saving and exposure reduction potential of the new smart materials in residential buildings under different climatic conditions.

Partners involved:


Stakeholders involved:

  • Manufacturers of building materials shall be involved regarding testing and possible co-development of products that have function to absorb indoor pollutants.
  • Building designers, health organizations and technological institutes who make testing for industry and run their labelling systems are also among potential stakeholders.


D3.1 A database of emission and transport properties of the smart materials developed in the project for active IAQ control.

D3.2 Mechanistic models for estimating the energy saving and exposure reduction potential of the new materials under realistic environmental conditions. The database and models will be published in scientific journal articles and in a project report.

D3.3 Chamber test data to validate the simulation model and confirm the effectiveness of selected smart materials or products.


Subtask 4 Ensuring performance of smart ventilation

Subtask Leader: France (Cerema, Gaëlle Guyot)
Co-Lead: Denmark (DTU, Jakub Kolarik)

This subtask focuses on practical conditions that assure reliable, cost effective and robust implementation of smart ventilation. This includes both installation and operation. A poor performance of smart ventilation systems can not only lead to waste of energy and aggravated IAQ. It can also create a bad reputation of smart ventilation among relevant stakeholders - designers, installers as well as occupants. This, in the end, can lead to adoption of more primitive, less efficient (in terms of energy use) and less effective (in terms of IAQ) forms of IAQ management. The subtask defines a smart ventilation according to the AIVC: “Smart ventilation is a process to continually adjust the ventilation system in time, and optionally by location, to provide the desired IAQ benefits while minimizing energy consumption, utility bills and other non-IAQ costs (such as thermal discomfort or noise). (…)”. Starting from this definition, demand-controlled ventilation (DCV) is considered as a specific subset of smart ventilation. This definition of smart ventilation includes a wide range of systems currently available in the literature and on the market depending on the type of sensing parameters (CO2, humidity, occupancy, …), the type of sensing combinations, the type of installation (centralized/decentralized) and the types of control algorithms. The subtask will also extend the scope to include alternative airflow distribution patterns and system configurations.


A4.1 Rating existing smart ventilation strategies

This activity includes a review of existing knowledge about IAQ and energy performances of residential smart ventilation, their cost, and the choice of several (max. 5-7) smart ventilation strategies being highlighted as promising from the review analysis. This analysis uses the rating method from ST 1, in relation to the source characterization in ST 2 and across typical climates for the involved partners’ countries.

A4.2 Quality control of implementation

The quality of ventilation systems is an issue of interest as the literature shows that ventilation is often badly designed, installed or used, achieving lower performances than expected (Boerstra, 2012)(Caillou et al., 2012)(Jobert and Guyot, 2013)(Guyot et al., 2017). This activity creates models and inspection protocols for assessing the IAQ and energy performances of smart ventilation systems, and ensuring the guaranteed performance over the lifetime of the IAQ management strategy.

The modelling stage of the work should complement the activity 4.1, identifying for the (5-7) selected smart ventilation strategies, the IAQ and energy benefits under different climate conditions, the pitfalls and crucial steps to be secured in an inspection protocol. This modelling stage could be accompanied by in situ campaigns in residential buildings equipped with smart ventilation.

Then, developed protocols should include installation and inspection schemes dedicated to smart systems, taking into account sensor quality testing, actuator durability, sensitivity to boundary conditions, etc. For Internet connected ventilation systems, models and protocols should quantify and allow the potential of continuous smart commissioning.

A4.3 Durability of smart ventilation systems and components:

Durability of building performances is still a general crucial issue to be addressed. With smart ventilation, we generally allow lower airflows at some times when needs are low (no occupancy, low emissions, etc…), but we have to secure even more than with other ventilation systems that expected ventilation airflows are still correctly provided, over the building life. This activity collects feedback from laboratory studies, in order to address the issue of durability of the sensors and the components, and in situ measurement campaigns in order to assess the robustness of the ventilation components and sensors to their use/lack of maintenance by occupants. We will also investigate the long term performance of sensors and other components, and the communication, visualization and automation issues.

This activity should be connected with the 4.2 activity in order to include in protocols requirements on sensors and components performance and durability.

A4.4 Occupant interaction

This activity maps the impact of interactions between the occupant and the system on the (perceived) performance and acceptability. It looks at options to visualize that the performance is as promised, feedback tools, controller design and demand response actions. We will here collect and analyze feedback from in situ campaigns of the 4.2 and 4.3 activities, and could complete them with complementary modeling approaches.

Partners involved:

Cerema (FR), DTU (DK), NUIG (IRe), ULille (FR), UGent (BE), KUL (BE), BBRI (BE), UoS (UK), CETIAT (FR), EPFL (CH), UAntwerp (BE), ULR (FR), BRANZ (NZ), UASoS (AT)

Stakeholders involved:

  • Manufacturers of smart ventilation shall be involved regarding testing and possible co-development of products that have function to absorb indoor pollutants.
  • Building designers, health organizations and technological institutes who do testing for industry and run their labelling systems are also among potential stakeholders.
  • Building operation managers, who will be a target group for developed protocols.


D4.1 Review report on IAQ and energy performance of smart ventilation (activity 4.1)

D4.2 Investigation report : IAQ and energy performances, durability and occupant interaction of 5 smart ventilation strategies

D4.3 Inspection protocol for residential smart ventilation ensuring the guaranteed performance over the lifetime


Subtask 5 Energy savings and IAQ: improvements and validation through cloud data and IoT connected devices

Subtask Leader: Belgium (UGent, Marc Delghust)
Co-Lead: France (ULille, Benjamin Hanoune)

This subtask is exploring the potential of the new generation of IoT connected devices (both standalone and embedded in eg. AHU’s) for smart IAQ management. What can we learn from big data? Can we benchmark system energy and IAQ performance based on this data? How can we make sure that the data is available and can be accessed? Can we update what we think we know about what happens in dwellings based on what we see in big data rollouts? What are the best protocols and ontologies? How to create viable services out of the data/business plans? How can we integrate data with smart grids?

These issues will be addressed by reporting experiences from a series of implementation case studies and overview of available (types of) datasets.

The implementation case studies will showcase state-of-the-art use of cloud data in the context of IAQ management systems regarding one or more of the following goals:

  • Continuous commissioning (see link with ST4): fault diagnosis, (preventive) maintenance
  • Continuous optimisation (see link with ST4): self-learning and -calibration, predictive control
  • 2-way user-centered communication: giving and getting feedback to and from users
  • Learning, developing and assessing: improving scientific knowledge, assessment frameworks and product development and targeting
  • New business cases or applications: e.g. related to aggregation and availability of IAQ or OAQ data-sets, offline and in real-time online, for R&D or real-time commissioning/optimisation/…

Some of the above mentioned goals are strongly related to some of the aims and activities of other subtasks. Within Subtask 5, the focus will be on the specific challenges from the use of IoT, as IoT offers great potential to support those activities. Added challenges studied in this subtask will include:

  • Discerning cause and effects (e.g. system biased results, availability of control groups or control(led) periods…)
  • Using external or indirect/deduced values (e.g. data from other, local or remote data sources to complement embedded sensors (from IAQ monitoring units, presence information from security systems, OAQ, weather forecasts...), balancing optimal use of those external sources and robustness/fall-backs)
  • Practical concerns: GDPR & IT (soft- & hardware): focussing on specificities of IAQ control strategies and related metrics (e.g. location and durability/calibration/… of sensors, instantaneousness) (for data communication and management protocols, see other IEA-EBC Annex 81)

It is not the aim of this subtask to deepen the knowledge on the separate fields of research related to these challenges in general (e.g. developing new state-of-the art communication protocols: see IEA-EBC Annex 81), but to focus on the specificities related to energy efficient IAQ management strategies in residential buildings.

Different types of activities will take place in this subtask. The first (Activity 5.1) will focus on classification and registration of existing data-sets relevant for the goals mentioned above (e.g. IAQ, OAQ, climate data…, real-time or not). The second type of activity will focus on the state-of-the art on use and application, in energy efficient IAQ management strategies, of IoT-supported devices, services and data (Activities 5.2 & 5.3).

Starting from a literature review on published state-of-the-art development and use of IoT in energy efficient IAQ management, we will collect new contributions from the participants on their recent and ongoing work in the field, stimulating the sharing of experience, findings and data. The latter allows other participants to test their developments on this data and to stimulate their developments as well as to identify new use cases. When reporting on the state-of-the-art, we will focus on the goals, added values and challenges mentioned above, distinguishing the goals relying specifically on online/(quasi) real-time availability, processing ànd use of IoT-data (Activity 5.3) from those making offline use of large IoT-data availability (Activity 5.2).

The three activities form also a basis of support for Subtasks 1-4 by allowing to define, in collaboration with the respective subtasks, relevant data and state-of-the-art applications that are relevant for e.g. testing and validation activities in those other subtasks.


A5.1 Classifying and registering relevant online data-sets for IoT-enabled energy efficient IAQ management systems and services.

The development of new solutions and business cases is strongly dependent on the availability of online data. This includes data on IAQ, but also on OAQ, outdoor climate, energy prices etc. Different factors define the usefulness and value of the data, such as spatio-temporal resolution, delay of availability, validation processes, inclusion of forecasting and accessibility of the data (protocols, open data or not). Within this activity, we will identify and classify the most relevant types of data and initiate a registry of relevant data sets and sources. When already available for certain types of data (e.g. outdoor climate), we will refer to the existing relevant classifications and registries, with a critical analysis from the point of view of energy efficient IAQ management strategies and in relation to metrics defined in Subtask 1.

A5.2 offline deduction and use of statistically representative data and knowledge for research, product/service development and performance assessment

This activity will focus on the state-of-the-art on the use of the same type of IoT based data as in Activity 5.3, but focussing on the value of the availability of such large-scale data sets for non-real-time analysis applications: to improve scientific knowledge, to improve and stimulate project and service development, and to improve performance assessment methods by e.g. the creation and use of statistically representative data sets, profiles etc. deduced from large scale IoT data. Challenges handled in this activity include discerning cause and effects in big data from (potentially product/location/… biased) data sets, defining the representativeness and applicability of the data-set, the use of complementary (separate/external) data sets and related data matching issues.

A5.3 online use of IoT data, devices and services

This activity will focus on the state-of-the-art on improved and continuous commissioning and optimisation of IAQ management systems using IoT sensors and cloud data. Examples of methods explored in this activity can be monitoring systems based on IoT sensors and/or internet enabled ventilation systems, which allow the building operator to identify operation anomalies like unbalanced airflows, malfunctioning by-pass in heat recovery or faulty sensors in particular rooms. These methods can be based on automated analysis of sensor data, potentially complemented with cloud-based interactions with the user (e.g. via apps).

Partners involved:

UGent (BE), DTU (DK), LU (UK), ULille (FR), CETIAT (FR), AAU (DK)

Stakeholders involved:

  • manufacturers offering IoT connected IAQ monitoring and management devices research institutes, governmental agencies and regulatory bodies for
  • conclusions from the data analysis
  • use cases of their data


5.1 Classification/reporting guideline on IoT-data for energy efficient IAQ management strategies

5.2 Overview registry of the most important online datasets for energy efficient IAQ management strategies

5.3 Report on the state of the art in offline IoT implementations for energy efficient IAQ management strategies

5.4 Report on the state of the art in online IoT implementations for energy efficient IAQ management strategies


Subtask 6 Dissemination, management and interaction

Subtask Leader: Denmark (DTU, Carsten Rode)
Co-Lead: Belgium (UGent, Jelle Laverge)

The final subtask assures the close alignment of the activities within the annex and the interaction with the AIVC. This subtask includes the outreach of the annex, eg. by managing the dedicated section of the IEA EBC webpage. It uses the different platforms that the AIVC provides to interact with the broader target audience. This task will also ensure the continuation of the link with (the results from) other ongoing and ended annexes, especially annex 68.


A6.1 Dissemination of the reports and results of the subtasks as TechNotes, Webinars or Ventilation nformation Papers (depending on the subject)

A6.2 Alignment of the Annex meetings with AIVC meetings

A6.3 Dedicated sessions during the AIVC conference

A6.4 Regular updates on the progress through the newsletters and social media activities

A6.5 Liaison with IEQ-GA and other annexes


6.1 Website

6.2 Newsletter items

Annex Info & Contact

Status: Ongoing (2020 - 2025)

Operating Agent

Dr Jelle Laverge
Assistant Professor
Ghent University
Department of Architecture & Urban Planning, Building Physics
Campus UFO T4, St-Pietersnieuwstraat 41
9000 Ghent
Tel: +32 9 264 37 49