Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 21st May 2022, 17:43:56 CEST

 
 
Session Overview
Session
Session T3.9 (Online Track): Improving indoor environmental quality
Time:
Thursday, 02/Sept/2021:
13:30 - 15:00

Session Chair: Veronica Soebarto, The University of Adelaide
Location: Virtual Meeting Room 3

External Resource: Click here to join the Zoom Meeting
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Presentations
13:30 - 13:37

Assessing the impact of outdoor air pollution on natural ventilation potential in major Indian cities

Kopal Nihar, Alex Nutkiewicz, Rishee K. Jain

Urban Informatics Lab, Stanford University, Stanford, USA

Aim and Approach

(max 200 words)

Natural ventilation (NV) is an effective passive cooling strategy to meet growing energy demands by leveraging natural wind flow. However, NV inevitably leads to higher air exchange rates. In places with high concentrations of outside air pollutants, NV can increase these pollutants in the indoor environment leading to adverse occupant health impacts. This problem is particularly important for developing nations in hot-humid climates like India that are forecasting significant rise in cooling demand [1] as well as deterioration of air quality in major cities [2].

We utilize publicly available data from OpenAQ to assess the impact of outdoor air pollution on NV potential (NVP). We look at outdoor PM2.5 concentration for major Indian cities i.e. New Delhi, Mumbai and Bangalore for 2019-20. We model a typical residential building representative of informal settlements in India using EnergyPlus. This is combined with a differential equation model to evaluate indoor exposure of occupants to outside pollutants. We consider two scenarios for utilizing NV supplemented by mechanical systems - one in which air pollutants constrain NV availability and the other without such considerations - and the results compared to quantify the potential reduction in NV usability and corresponding energy penalty.

Scientific Innovation and Relevance

(max 200 words)

Few works in the field have evaluated the impacts of outdoor air pollutants (particularly PM) on NVP but those that have undertaken this problem have focused primarily on commercial buildings. Authors of [3], [4] investigate the impact of ambient air pollution on energy savings through NV for commercial buildings in major cities of China and USA respectively.

Our approach aims to extend this to the residential sector with a particular focus on informal settlements; as occupants in such settlements are highly susceptible to the health risks of poor air quality. With over 881 million people living in informal settlements across the world [5], a minor improvement in space cooling and/or air pollution exposure has significant implications. Our initial analysis focuses primarily on large cities in India as many cities (particularly New Delhi and its neighbors) dominate the list of world’s most polluted cities and have significant populations of informal settlement dwellers [6]. To accomplish this, we propose a modeling approach that integrates highly granular publicly available air pollution data with a coupled EnergyPlus and differential equation air-flow model to evaluate how NVP for space cooling changes when air pollution exposure is taken into account.

Preliminary Results and Conclusions

(max 200 words)

We simulate the proposed model for the month of August’19 in New Delhi, Mumbai and Bangalore. We evaluate the impact on NV against a standard threshold of 25 ?g/m3 average daily PM2.5 level as per World Health Organization (WHO) Air Quality guidelines. We observe that favourable hours for NV consistently decrease across all cities when PM2.5 levels increase. Maximum impact is observed for New Delhi where NVP is reduced by 75% whereas minimum impact is observed in Bangalore where NVP is reduced by only 20%. The corresponding reduction in cooling energy saving potential is observed to be 10% for New Delhi and 3% for Bangalore. Therefore, our results indicate that outdoor PM2.5 levels have a significant impact on NVP especially in highly polluted cities like New Delhi. As such, our work points to the synergistic opportunities that exist for reducing both air pollution and energy consumption in informal settlement residential dwellings across major Indian Cities. We conclude that paving a path towards a more sustainable energy future will require balancing tradeoffs between occupant thermal comfort, air pollution exposure and building energy usage in urban areas across the world.

Main References

(max 200 words)

[1] Kumar, S., Sachar, S., Kachhawa, S., Goenka, A., Kasamsetty, S., George, G. (2018). Demand Analysis of Cooling by Sector in India in 2027. New Delhi: AEEE

[2] Guttikunda, S. K., Goel, R., & Pant, P. (2014). Nature of air pollution, emission sources, and management in the Indian cities. Atmospheric environment, 95, 501-510

[3] Tong, Z., Chen, Y., Malkawi, A., Liu, Z., & Freeman, R. B. (2016). Energy saving potential of natural ventilation in China: The impact of ambient air pollution. Applied energy, 179, 660-668

[4] Chen, J., Brager, G. S., Augenbroe, G., & Song, X. (2019). Impact of outdoor air quality on the natural ventilation usage of commercial buildings in the US. Applied Energy, 235, 673-684

[5] UN-HABITAT. SLUM ALMANAC 2015 2016 Tracking Improvement in the Lives of Slum Dweller; 2016:1–82.

[6] IQAir. 2019 World Air Quality Report: Region and City PM2.5 Ranking



13:37 - 13:44

Simulating the impact of ventilation on viral infection probability from aerosol transmission in enclosed spaces

Olivia Nile Sobek1, Parag Rastogi1, John Allison1, Graeme Jephson1, Chris Pyke2, Alan Wegienka1

1arbnco ltd, United Kingdom; 2Arc Skoru

Aim and Approach

(max 200 words)

This study uses simulation to assess the impacts of occupant density and ventilation rates as control measures to reduce the risk of aerosol transmission of viruses (e.g., influenza, rhinovirus, SARS, and COVID-19) in large and small offices. The simulation outputs are selected to correspond with in-situ CO2 sensors and control points for implementation in offices around the world. The results of the simulation can be used to set targets for CO2 and other parameters that can be easily measured by low-cost sensors to manage risk of infection due to aerosol transmission.

We used CONTAM 3.2 to simulate small and large workspaces from very low to very high occupancy, representing socially-distanced office use and events such as conferences respectively. Each simulation was run with a standard AHU using ventilation rates from CIBSE Guide A [1] for the length of a typical workday. Occupants were assumed to generate CO2 at rates typical of office work. Using an augmentation of the Wells–Riley equation [2], we coupled these CO2 concentrations with known quantum generation rates of influenza, rhinovirus, and SARS, and preliminary rates of COVID-19 [3]. Finally, we assumed a number or fraction of infected people to model the probability of indoor airborne transmission.

Scientific Innovation and Relevance

(max 200 words)

While studies have used the augmented Wells-Riley equation to model the risk of viruses at different ventilation levels e.g., [2], few have systematically looked at occupant density or ventilation as forms of control strategies or examined office spaces. By concentrating on offices and varying the number of occupants under four ventilation rate categories [1], we are able to model the impact of proposed social-distancing and ventilation control measures to reduce airborne infection of COVID-19, and similar viruses like SARS-CoV-1. This has implications beyond COVID-19, as similar strategies and behaviour applies for regular seasonal viruses like influenza and rhinovirus. This study does not suggest that aerosol transmission is the only transmission route, or that it is the most important. Rather, the idea is that all else held constant, infection probabilities are reduced with these actions.

This data can be used to suggest evidence-driven back-to-work protocols for buildings based on their ventilation systems and number of occupants. This could help tune ventilation rates or occupancy reductions to lower infection probability while maximising safe utilisation. Further, combining these parameters with continuous monitoring could allow infection probabilities to be calculated in real-time and immediate corrective actions from an HVAC system to lower infection probabilities.

Preliminary Results and Conclusions

(max 200 words)

The results of this study clearly show a trend of decreased probabilities of viral infections with increased ventilation and decreased occupant density. There is a clear trend of increased viral infection probability with an increase in the number of infected people, and a decrease in the probability with an increase in ventilation. Increasing ventilation rates from 5 L/s/person to 20 L/s/person a decrease in the probability of infection from 2.3% to 43% can be seen depending on the virus and occupant density. Increasing the number of infected people within the same ventilation rate increased the probability anywhere from 5.5% to 50%. When the occupant density was halved with all other factors kept the same, the infection probability decreased between 0.1% and 6%. These results show the effectiveness of current social-distancing and ventilation-based virus control strategies, suggesting that by incorporating them into their back-to-work protocols an office space could substantially lessen the risk of infection to its occupants. By quantifying the probabilities of these strategies’ effectiveness in typical office settings our study suggests the potential to model and simulate more complicated infectious circumstances and use real-time CO2 readings, to give building-specific advice on ventilation rates and occupancy.

Main References

(max 200 words)

[1] CIBSE, Environmental design: CIBSE guide A, Eighth edition. London: Chartered Institution of Building Services Engineers, 2015.

[2] S. N. Rudnick and D. K. Milton, ‘Risk of indoor airborne infection transmission estimated from carbon dioxide concentration’, Indoor Air, vol. 13, no. 3, pp. 237–245, Sep. 2003, doi: 10.1034/j.1600-0668.2003.00189.x.

[3] H. Dai and B. Zhao, ‘Association of infected probability of COVID-19 with ventilation rates in confined spaces: a Wells-Riley equation based investigation’, Emergency Medicine, preprint, Apr. 2020. doi: 10.1101/2020.04.21.20072397.



13:44 - 13:51

Interactive Sankey diagram for visualizing real-time and/or simulation-derived energy flows

Claude Demers-Bélanger1, Adam Rysanek2

1University of British Columbia, Canada; 2School of Architecture and Landscape Architecture, University of British Columbia, Canada

Aim and Approach

(max 200 words)

As UBC keeps adding buildings and students while putting forward a desire to be one of the greenest university campuses in Canada and in the world, energy management (UBC's Energy & Water Services, 2020) and decision-making toward energy become a major issue. The decision-makers need access to relevant information in a timely matter. This is an issue that not only UBC faces, but many institutions and district energy systems globally. This paper presents a new data analysis tool for visualizing district energy flows at varying scales, in real-time, via an interactive Sankey energy flow diagram. In the pilot application, the tool makes use of UBC’s energy and water services (EWS) data collected with smart meters across the university’s portfolio of over 150 buildings. The visualization tool allows users to inspect individual building energy use, by end-use, as well as download energy use data as visualized

Scientific Innovation and Relevance

(max 200 words)

Sankey diagrams have been a common way to visualize flows of information. From economic flows (Bakenne, Nuttal, & Kazantzis, 2016) to energy sources (Cullen & Allwood, 2010), these charts have been able to help decision-makers better understand the relationship between dynamic systems driven by the flow of resources from source to end-use. Studies have shown that with better visualization, decision-makers can improve overall building performance diagnostics without having to resort or rely on building automation systems. Sankey diagrams can provide transparent carbon accounting of building energy use across end uses, building types, and time. Sankey diagrams also make access to measured building performance data more accessible, which can support practitioners seeking to calibrate building energy mdels. (Coakley, Raftery, & Keane, 2014). Though representing energy flows via a Sankey diagram is now new, we argue that an interactive Sankey diagram that is connected to a real-time building metering database, or a synthetic simulation-derived database, can improve one’s understanding of measured and/or predicted building performance.

Preliminary Results and Conclusions

(max 200 words)

The data is pulled from a time series specific data base (InfluxDB) and the web app will be delivered in JavaScript using D3.js module to create the Sankey diagrams. A full working example of the tool as well as a downloadable Github repository will be provided at the full submission of this paper.

Main References

(max 200 words)

Bakenne, A., Nuttal, W., & Kazantzis, N. (2016). Sankey-Diagram-based insights into the hydrogen economy of today. International Journal of Hydrogen Energy, 7744-7753.

Coakley, D., Raftery, P., & Keane, M. (2014). A review of methods to match building energy simulation models to measured. Renewable and Sustainable Energy Reviews, 123-141.

Cullen, J. M., & Allwood, M. J. (2010). The efficient use of energy: Tracing the global flow of energy from fuel. Energy Policy, 75-81.

Lee, D., Cha, G., & Park, S. (2016). A Study on Data Visualization of Embedded Sensors for Building Energy Monitoring using BIM. INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 807-814.

Motegi, N., & Piette, M. (2002). Web-based energy information systems for large commercial buildings. National Conference on Building Commissioning.

Niu, S., Pan, W., & Zhao, Y. (2015). A BIM-GIS Integrated Web-based Visualization System for Low Energy Building Design. 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) and the 3rd (pp. 2184-2192). Procedia Engineering 121.

UBC's Energy & Water Services. (2020, July 14). Energy Management. Retrieved from UBC'S Energy & Water Story: https://energy.ubc.ca/about-us/ubcs-story/energy-management/



13:51 - 13:58

Indoor environmental quality (IEQ) assessment using laser-assisted data acquisition (LADA) of building geometry

Simon Wyke, Lasse Rohde, Rasmus Lund Jensen

Department Of The Built Environment, Aalborg University, Denmark

Aim and Approach

(max 200 words)

A new Indoor environmental quality (IEQ) assessment tool has been developed, to perform fast and inexpensive IEQ-evaluations, and performance dissemination that is more understandable for all stakeholders (Steen Larsen et al., 2019). This paper investigates how data for this new IEQ assessment tool can be collected efficiently through laser assisted data acquisition (LADA) of building geometry.

We tested LADA for the newly developed IEQ-evaluation tool on two flat units, and exported the acquired data from a handheld laser meter, used as a high-tech measuring tape, to the IEQ-tool, using data processing software, in which building geometries were modelled in 2D and exported into the Comma Separated Value format (CSV).

Scientific Innovation and Relevance

(max 200 words)

IEQ is currently receiving increased attention in the building industry. However, IEQ is a multifaceted topic consisting of both thermal, visual, acoustic and air quality aspects, and their potential impact on occupant comfort, health and well-being (Rohde, Larsen, Jensen, & Larsen, 2019). This multifaceted nature makes IEQ difficult to comprehend for many building owners and occupants, and makes overall IEQ assessments a complicated, time-consuming and costly task.

Various building data are needed in order to evaluate the IEQ-potential of a building. It can be time-consuming and expensive to acquire, model and store such data in a repository e.g. a spreadsheet schema or building information model (BIM). Building models based on manual data input, additionally, often lack consistency and reliability (Gerrish, Ruikar, Cook, Johnson, & Phillip, 2016).

Preliminary Results and Conclusions

(max 200 words)

Data acquisition for IEQ can be based on automated processes, including use of both photogrammetry and laser meters. Many studies focus on how to apply photogrammetry point-clouds to generate as-build models (Barazzetti, 2016). However, in this study we used a handheld laser meter to do laser assisted data acquisition (LADA), to limit the amount of data which needs to be acquired, to the exact needs of an IEQ-evaluation tool (Swanström Wyke, Svidt, Jønsson, Rohde, & Lund Jensen, 2019).

The test revealed that it was possible to acquire accurate geometry-data for IEQ-evaluation based on LADA, with low-effort and sufficient accuracy for the relevant IEQ-simulations.

Main References

(max 200 words)

Barazzetti, L. (2016). Parametric as-built model generation of complex shapes from point clouds. Advanced Engineering Informatics, 30(3), 298–311. https://doi.org/10.1016/j.aei.2016.03.005

Gerrish, T., Ruikar, K., Cook, M., Johnson, M., & Phillip, M. (2016). Engineering , Construction and Architectural Management Article information : To cite this document : Trends of Skills and Productivity in the UK Construction Industry, 15(4), 372–382. https://doi.org/10.1108/eb021087

Rohde, L., Larsen, T. S., Jensen, R. L., & Larsen, O. K. (2019). Framing holistic indoor environment: Definitions of comfort, health and well-being. Indoor and Built Environment, 0(0), 1–19. https://doi.org/10.1177/1420326X19875795

Swanström Wyke, S. C., Svidt, K., Jønsson, K., Rohde, L., & Lund Jensen, R. (2019). Laser-Assisted Data Acquisition Of Building Geometry: Selection and use of laser meters and data processing software for IV20. Aalborg, Denmark.



13:58 - 14:05

Towards safer work environments during the COVID-19 crisis: A Study of different floor plan layouts and ventilation strategies coupling open foam and airborne pathogen data for actionable, simulation-based feedback in design

Zoe De Simone, Patrick Kastner, Timur Dogan

Cornell University, United States of America

Aim and Approach

(max 200 words)

As work environments struggle to reopen during the current COVID-19, it is crucial to establish tools for effective decision making. While a strong emphasis has been placed on determining general guidelines to reduce the risk of airborne viral spread (1),(2),(3),(4) there is a lack of open and easy to use simulation workflows to quantify the risk of airborne pathogens indoors on a case-to-case basis as floor-plans, furniture layouts, and ventilation inlet-outlet locations have a significant impact on indoor air movement.

We couple Computational Fluid Dynamics (CFD) passive scalar transport (5) with an energy model and viral decay function (6) to quantify the exposure to airborne pathogens by integrating this approach in the popular Rhino3d/Grasshopper CAD environment. We further demonstrate its application in a comparative study that investigates how plan and furniture layout, including small scale interventions such as plexiglass partitions and distancing measures, natural and hybrid ventilation strategies can impact the movement of airborne pathogens in indoor environments. The aim of the project is to enable stakeholders to assess existing workplaces, and to enable designers to create spaces that are more resilient in an epidemic.

Scientific Innovation and Relevance

(max 200 words)

Spatial proximity, interpersonal contact, superficial contact and respiratory droplets are the four recognized means of indoor viral spread (7). While coughing and sneezing produce larger airborne particles that decay within 1s (8), small particles, produced when breathing, decay within 8 to 14 minutes (9). Once airborne, small droplets dehydrate, slowing their fall(10) and decay at rates ranging between 30 to 177 min depending on microclimate boundary conditions(11), including ventilation rates (8), thermal and radiation environment.

To model environmental factors such as air movement, thermal and uv exposure of small viral aerosol particles we introduce a new metric that uses the combination of passive scalar aerosol tracer, and an energy model coupled viral decay function (6).

Although ventilation reduces pathogen concentration, airflow patterns and direction can increase the risk of transmission between occupants. Reported transmission routes at a restaurant (12) demonstrate the importance of simulating seating arrangements and ventilation systems simultaneously. Our tool enables users to analyze the effectiveness of spatial layouts and ventilation strategies, providing architects with ways of validating muti-factor design strategies. Finally, we demonstrate the fidelity of the new metric by conducting case studies comparing venitation and floor plan layouts.

Preliminary Results and Conclusions

(max 200 words)

The current health crisis suggests a clear need for easy-to-use health risk modeling tools for architects and engineers to evaluate the design of interior spaces. While existing guidelines offer general instructions regarding increased ventilation and distancing measures such as workstation configurations and plexiglass partitions (13), only few easy to use tools for ad-hoc evaluations of workplace configurations exist. In addition, the combination and efficacy of such guidelines has not been analyzed for a design space with multiple variables, such as floor plan layouts and ventilation direction. To understand the efficacy of existing spatial and ventilation guidelines on the mitigation of an airborne virus, the project investigates a series of typical office space layouts and ventilation systems(14).

We implement a prototypical design evaluation workflow that couples OpenFOAM, EnergyPlus, Eddy3D with the SARS-CoV-2 Airborne Decay Calculator (6). This allows users to simulate potential pathogen exposure levels in different scenarios considering layouts and ventilation strategies. A series of case studies evaluate floor plans with open space and partitioned spaces with mixed, displacement and natural ventilation. Preliminary results show significant differences between the test cases and suggesting promising fidelity of the proposed simulation methodology.

Main References

(max 200 words)

1. ASHRAE, Indoor air quality guide. 2009.

2. HVAC in the context of COVID-19. 2020.

3. Stewart, Schoen, Mead, Olmsted, Sekhar, Vernon, ASHRAE Position Document on Infectious Aerosols. 2020.

4. WHO, Getting your workplace ready for COVID-19: How COVID-19 spreads, 2020.

5. Kastner , Dogan, A cylindrical meshing methodology for annual urban computational fluid dynamics simulations. 2020

6. Department of Homeland Security. Estimated Airborne Decay of SARS-CoV-2 2020.

7. Dietz , Horve, Coil , Fretz , Eisen , Van Den Wymelenberg, Novel Coronavirus (COVID-19) Pandemic: Built Environment Considerations ... 2020.

8. Somsen, van Rijn, Kooij, Bem, Bonn, Small droplet aerosols in poorly ventilated spaces and SARS-CoV-2 transmission. 2020.

9. Stadnytskyi , Bax, Bax , Anfinrud, The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. 2020.

10. Wells W. ON AIR-BORNE INFECTION. 1934.

11. Smither, Eastaugh, Findlay, Lever. Experimental aerosol survival of SARS-CoV-2 in artificial .... Emerg Microbes Infect. 2020.

12. Lu, Gu , Li , Xu , Su , Lai , COVID-19 Outbreak Associated with Air Conditioning in Restaurant, Guangzhou, China, 2020

13. CDC. Communities, Schools, Workplaces, & Events. 2020.

14. Atkinson J, Chartier Y, Pessoa-Silva CL, Jensen P, Li Y, Seto W-H. Concepts and types of ventilation. 2009.



14:05 - 14:12

Computational analysis of aerothermal characteristics of skygardens and comparative assessment of different attenuators

Murtaza Mohammadi, Paige Wenbin Tien, John Kaiser Calautit

University of Nottingham, United Kingdom

Aim and Approach

(max 200 words)

The main aim of this study was to analyse the impact of vegetation on the aero-thermal conditions in a skygarden located on a high-rise building. The aerodynamic response of skygarden vegetation must be understood in detail to assist designers in the selection and arrangement of species and buffer elements for creating a conducive environment for occupants. The current study utilises Computational Fluid Dynamics (CFD) to analyse nine different vegetation configurations inside a high-rise skygarden to determine the aero-thermal performance. Simulations were carried out using the Steady Reynolds Averaged Navier–Stokes (SRANS) equation, where the vegetation was modelled as a porous zone with cooling capacity [1,2,3].

Scientific Innovation and Relevance

(max 200 words)

Green spaces can greatly improve the quality of life, especially in urban areas, by influencing the health and well-being of citizens. It acts as a filter which reduces pollutants, dust and other harmful particles from the surrounding air, and are frequently planted as a barrier between emission source and adjacent areas to improve air quality. Additionally, vegetation can improve the local thermal condition by enabling shading and evaporative cooling [4,5]. Thus, building applied vegetative measures, such as green walls and roofs, are increasingly gaining popularity due to multifold benefits.

One such architectural feature, the skygarden, is an important design intervention for improving the social, economic and environmental values of a building. Many high-rise buildings feature a skygarden as a social space, allowing occupants to connect and experience outdoor freshness within a semi-enclosed environment [6]. Moreover, the current pandemic has forced designers to re-examine the spatial planning principles of built spaces, mainly due to health and safety concerns. Vegetation, including hedges, shrubs and trees can act as surfaces for deposition of aerosol particles [7], which are considered to be the primary mode of microbe transmission [8, 9]. Our study analyses the various attenuation effects of trees, primarily wind speeds and temperature.

Preliminary Results and Conclusions

(max 200 words)

The results indicate that trees can attenuate high wind speeds in the skygarden for most of the configurations. Especially from the center to the leeward edge of the skygarden, wind speeds are within the comfort range at occupants’ height. Near the windward edge, however, some amplification occurs due to constriction created in the air flow stream. The trees are able to extract heat from air and create cooling effect in their wake, up to 1°C. The effect is stronger with higher number of trees and reduced air speed.

Although the absolute performance of the skygarden improves with a higher number of trees, when taking into account the ground cover of trees, the relative performance of a single-row configuration is found better than a double row of trees. Thus, the design of the skygarden, requires an optimal balance between the benefits from the trees and the spatial cost of plantation.

Main References

(max 200 words)

1. Kichah, A., Bournet, P.-E., Migeon, C., Boulard, T., Measurement and CFD simulation of microclimate characteristics and transpiration of an Impatiens pot plant crop in a greenhouse

2. Gromke, C., Blocken, B., Janssen, W., Merema, B., van Hooff, T., Timmermans, H., CFD analysis of transpirational cooling by vegetation: Case study for specific meteorological conditions during a heat wave in Arnhem, Netherlands

3. Manickathan, L., Defraeye, T., Allegrini, J., Derome, D., Carmeliet, J., Parametric study of the influence of environmental factors and tree properties on the transpirative cooling effect of trees

4. Imran, H. M., Kala, J., Ng, A.W. M., Muthukumaran, S., Effectiveness of vegetated patches as Green Infrastructure in mitigating Urban Heat Island effects during a heatwave event in the city of Melbourne

5. Besir, A. B., Cuce, E., Green roofs and facades: A comprehensive review, Renewable and Sustainable Energy Reviews

6. Pomeroy, J., The Skycourt and Skygarden, Greening the Urban Habitat

7. S. Janhäll, “Review on urban vegetation and particle air pollution - Deposition and dispersion,”

8. WHO, “DAMPNESS AND MOULD WHO GUIDELINES FOR INDOOR AIR QUALITY,” 2009.World Health Organization



14:12 - 14:19

The impact of PCM applications on thermal comfort in standardized preschool designs in 2 Brazilian climatic zones

Lorenzo Olivo Filippini, Gabriela Sartori, Maurício Carvalho Ayres Torres

Universidade Federal do Rio Grande do Sul, Brazil

Aim and Approach

(max 200 words)

The Federal Brazilian Government's Proinfância Program was developed in 2007 to ensure children's access to preschools. Preschool buildings' standardized plans were developed to speed up licitation processes. Since its creation, more than 5 billion Brazilian Reais had been invested, while more than 5,600 preschools of all types had already been built or were being built until 2017. According to the ABNT NBR 15.220-3:2005 criteria, Brazil is divided into eight bioclimatic zones. The standardized preschool building plan must observe the distinct climatic zones’ characteristics to comply with thermal performance minimum levels. The thermal performance of buildings is directly associated with the occupant's thermal comfort. For thermal comfort maintenance, the use of Phase Change Materials (PCM) is a possible strategy that might be adopted to increase the thermal inertia of buildings envelope and reduce the thermal amplitude of indoor environments. This study aims to assess the impact of the PCM use on the thermal comfort in the preschool classrooms, according to the ASHRAE Standard 55-2013 Adaptive Model. Thermal simulations were performed using EnergyPlus, considering three different types of PCMs in four distinct applications for two bioclimatic zones in Brazil.

Scientific Innovation and Relevance

(max 200 words)

Since there is no standard about thermal comfort in schools in Brazil, this research is a pioneer for assessing thermal comfort in naturally ventilated standardized preschool building plans in two specific bioclimatic zones in Brazil - 1 and 8 - using the ASHRAE Standard 55-2013 Adaptive Model. This study is also a pioneer for assessing the impact of using PCM to increase thermal comfort in the preschool standardized plan. The adoption of PCM might promote the improvement of thermal comfort levels in Brazilian preschool buildings, as well as, reduce the energy consumption in public buildings. Considering that more than 5,600 schools based on standardized designs had been built all over the country, the improvement of thermal comfort levels can impact a great number of children and save a significant amount of energy. Also, the results achieved in this study might encourage the development of a thermal comfort regulation for preschool buildings in Brazil, granting good quality learning spaces for children.

Preliminary Results and Conclusions

(max 200 words)

The materials improved the thermal comfort by decreasing the operative temperature to different levels in each application in the prolonged occupancy spaces. The results indicate that, in the warmest Brazilian bioclimatic zone - zone 8 -, the impact of the PCM application was more relevant, reducing the thermal discomfort levels regarding heat. While in the Brazilian bioclimatic zone with more intense winter - zone 1 - the PCM application showed less relative reduction of the thermal discomfort levels. Results show that the average relative reduction in the total number of days in thermal discomfort conditions was approximately 7%, for zone one, and approximately 57% for zone eight. In the absolute number of days in thermal discomfort levels, those values represent an average reduction of 11,5 days, for zone 1, and 15 days, for zone 8. Considering both of these zones, it is perceived the capacity that the chosen PCMs have to passively improve the thermal comfort aspects towards the preschool occupants. Thus, PCMs might be an interesting alternative to air conditioning systems, especially in warmer climates.

Main References

(max 200 words)

AL-JANABI, Ali; KAVGIC, Miroslava. Application and sensitivity analysis of the phase change material hysteresis method in EnergyPlus: A case study. Applied Thermal Engineering, v. 162, p.1-19, nov. 2019. Elsevier BV. http://dx.doi.org/10.1016/j.applthermaleng.2019.114222.

MAGENDRAN, Suhanyaa S. et al. Synthesis of organic phase change materials (PCM) for energy storage applications: A review. Nano-structures & Nano-objects, v. 20, p.1-18, out. 2019. Elsevier BV. http://dx.doi.org/10.1016/j.nanoso.2019.100399.

PAROUTOGLOU, Evdoxia et al. A PCM based cooling system for office buildings: a state of the art review. E3s Web Of Conferences, v. 111, p.1-8, 2019. EDP Sciences. http://dx.doi.org/10.1051/e3sconf/201911101026.

TABARES-VELASCO, Paulo Cesar; CHRISTENSEN, Craig; BIANCHI, Marcus. Verification and validation of EnergyPlus phase change material model for opaque wall assemblies. Building And Environment, v. 54, p. 186-196, ago. 2012. Elsevier BV. http://dx.doi.org/10.1016/j.buildenv.2012.02.019.

WEINLÄDER, Helmut; BECK, Andreas; FRICKE, Jochen. PCM-facade-panel for daylighting and room heating. Solar Energy, v. 78, n. 2, p. 177-186, fev. 2005. Elsevier BV. http://dx.doi.org/10.1016/j.solener.2004.04.013



14:19 - 14:26

Building meta-optimization: A study on the reuse of previous simulation data to reduce computational costs

Lucas Camilotti, Nathan Mendes, Roberto Zanetti Freire

Pontifical Catholic University of Paraná, Brazil

Aim and Approach

(max 200 words)

The proposed study aims to evaluate the benefits of data reuse in-between different simulation procedures during a whole-building energy optimization process. A simulation can be broken down into distinct tasks, and some of them may provide the same results when similar input parameters are adopted. Considering the scenario of a parametric optimization problem, where similar design vectors can be generated, the potential of data reuse between different simulations is present. As simulations are usually treated individually, without the exchange of information among them, this can cause a waste of time and resources by performing redundant tasks that could be avoided. To confirm our statements, we use the Domus software, a certified Brazilian tool for building energy analysis, as a case study considering a practical application of the proposed approach. One of the costliest stages during simulations using Domus is the view factor calculation [1], which is used to estimate the radiation which leaves a surface that strikes another, a calculation that depends exclusively on their geometry. By applying the proposed approach to building energy modeling optimization, the view factor obtained from previous simulations could be reused, providing significant improvement in time during the optimization process.

Scientific Innovation and Relevance

(max 200 words)

Many attempts to reduce the execution time of costly optimization problems associated with whole-building simulation are constantly being performed. Methodologies such as parallelization and scheduling of simulations in a distributed environment [2,3], or the use of surrogate models for both the approximation of the objective function and the reduction on the number of simulations [4,5], have shown successful results in this field. The proposed methodology can provide yet another point of improvement, enabling yet another layer of reduction in overall optimization time. An evaluation of the proposed methodology in junction with the two aforementioned ones can provide useful insight in how well the three approaches work alongside one another. Moreover, most building simulation tools were not designed oriented to parametric optimization as a premise during the development stage, and as a result, minimal efforts were directed to provide integration points and functionalities for generic optimization tools. Based on that, the methodology has the additional benefit of encouraging simulation program developers to provide more fine-grained control over simulations, giving more freedom to the optimization tools utilized.

Preliminary Results and Conclusions

(max 200 words)

The methodology was applied over a wide number of benchmark building design optimization problems, with selected design variables ranging from completely geometry-related, to completely unrelated, to a mix of both. The optimization processes were carried out multiple times for each problem, in order to obtain an average result and eliminate possible outliers. Preliminary results showed that the methodology has no weaknesses, aside from implementation complexity. Improvements showing a considerable reduction in overall optimization time were reported, with worst-case scenarios showing no changes, but also no penalties. Additionally, the view factor data reuse was carried out in cases of equal values for the whole-building, and an evaluation considering individual thermal zones separately has yet to be done, to verify if an even finer-grained control will prove to be beneficial for the whole-building optimization process.

Main References

(max 200 words)

1. Augusto LD de C, Giacomet B, Mendes N. Numerical method for calculating view factor between two surfaces. In: IBPSA 2007 - International Building Performance Simulation Association 2007. 2007. p. 269–74.

2. Yang C, Li H, Rezgui Y, Petri I, Yuce B, Chen B, et al. High throughput computing based distributed genetic algorithm for building energy consumption optimization. Energy and Buildings. 2014;76:92–101.

3. Labib R, Baltazar J-C. Using Python to Automate the Preparation and Execution of Thousands of Daylighting and Glare Simulations on a Cloud Parallel Computing environment for Time-efficient Simulations. In: Proceedings of Building Simulation 2019: 16th Conference of IBPSA. 2020. p. 1545–51.

4. Carreras J, Pozo C, Boer D, Guillén-Gosálbez G, Caballero JA, Ruiz-Femenia R, et al. Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling. Energy and Buildings. 2016;130:506–18.

5. Barnes EC, McArthur JJ. Building Energy Use Surrogate Model Feature Selection – A Methodology Using Forward Stepwise Selection and LASSO Regression Methods. In: Proceedings of Building Simulation 2019: 16th Conference of IBPSA. 2020. p. 3078–85.



14:26 - 14:33

Assessing performance of passive or low-energy resilient cooling technologies for pre-1980 medium office buildings in Phoenix, Arizona and Chicago, Illinois

Nari Yoon1, Sang Hoon Lee1, Edward Arens2, Hui Zhang2, Ronnen Levinson1

1Lawrence Berkeley National Laboratory, USA; 2University of California, Berkeley, USA

Aim and Approach

(max 200 words)

Our research evaluates a comprehensive set of key performance indicators (KPIs) for passive or low-energy building technologies (hereinafter, “strategies”). The strategies include cool envelope materials to reduce solar heat gain, natural ventilation to remove excessive indoor heat, and personal comfort systems to allow the cooling set point to be increased. They seek to provide resilient cooling for buildings, and thereby help building occupants adapt to increasingly frequent extreme heat events.

We used the whole-building energy simulation tool EnergyPlus to model prototype buildings before and after application of each strategy. Annual hourly HVAC energy use and indoor environmental conditions were used to calculate KPIs for each floor, for each multi-story perimeter/core zone, and for the whole building. We computed more than 50 KPIs and evaluated diagnostics (unit tests) to check their validity.

Major KPIs reported in this study include changes in annual heating, ventilation, and cooling energy uses; changes in seasonal and annual very-hot, warm, cool, and very-cold exceedance hours that gauge the number of discomfort occupied hours; and changes in seasonal and annual weighted exceedance hours, which are the sums of positive values of predicted mean vote (PMV) exceedance over occupied hours.

Scientific Innovation and Relevance

(max 200 words)

We assessed how each strategy could make “building occupants” more resilient to hot weather, boosting comfort, health, and productivity; and make “building cooling systems” more resilient to hot weather, improving their ability to meet cooling load. These strategies could reduce illness and death in disadvantaged communities where residents lack air conditioning, and benefit any community subject to scheduled or unscheduled power outages. Our analysis of the selected strategies can thus inform to what extent a building and occupants may resist the consequences, reduce the impact, and recover from extreme weather conditions.

When expanded to a wide range of building types, vintages, sizes, and climates, our KPI approach can identify strategies that provide high cooling resilience under given conditions.

Preliminary Results and Conclusions

(max 200 words)

We examined three-story medium office prototype buildings, each built prior to 1980, in Chicago, Illinois (cold winter / hot-humid summer) and Phoenix, Arizona (hot/dry both seasons).

- The annual fan-plus-cooling energy savings always exceeded the annual heating energy penalty.

- HVAC savings varied linearly with some parameters, but not with others. For example, cool-roof savings were proportional to increase in roof albedo, while ceiling-fan savings rose non-linearly with increase in average air speed.

- The annual comfort KPIs varied by region within the building. For example, in an uncooled three-story building, the thermal-comfort improvement (reduction in weighted warm exceedance hours) on the top floor was about 17-19 times that on the bottom floor when applying the cool-roof strategy.

- The annual HVAC savings yielded by ceiling fans and cool roofs in Phoenix were 3-4 times those in Chicago, and the annual thermal-comfort improvements made by these strategies in Phoenix were 2.5-3 times those in Chicago.

The comprehensive KPIs quantified cooling resilience of a building, providing detailed insights on how the selected strategies influence the energy and comfort performance of building zones and the whole building. We also demonstrated the ability to check building simulation with unit tests.

Main References

(max 200 words)

ASHRAE. (2017). ANSI/ASHRAE Standard 55-2017: Thermal Environmental Conditions for Human Occupancy. https://www.ashrae.org/technical-resources/bookstore/standard-55-thermal-environmental-conditions-for-human-occupancy

Borgeson, S., & Brager, G. (2011). Comfort standards and variations in exceedance for mixed-mode buildings. Building Research & Information, 39(2), 118–133. https://doi.org/10.1080/09613218.2011.556345

Rosado, P. J., & Levinson, R. (2019). Potential benefits of cool walls on residential and commercial buildings across California and the United States: Conserving energy, saving money, and reducing emission of greenhouse gases and air pollutants. Energy and Buildings, 199, 588–607. https://doi.org/10.1016/j.enbuild.2019.02.028

Hoyt, T., Arens, E., & Zhang, H. (2015). Extending air temperature setpoints: Simulated energy savings and design considerations for new and retrofit buildings. Building and Environment, 88, 89–96. https://doi.org/10.1016/j.buildenv.2014.09.010

Yoon, N., Norford, L., Malkawi, A., Samuelson, H., & Piette, M. A. (2020). Dynamic metrics of natural ventilation cooling effectiveness for interactive modeling. Building and Environment, 180, 106994. https://doi.org/10.1016/j.buildenv.2020.106994



14:33 - 14:40

Optimum daylighting and thermal comfort simulation framework for school buildings in hot arid climate

Muhammad Adel Ahmed Mahmoud1, Medhat Dorra1, Khaled Nassar2, Ayman Hassan El Hakea3, Mohamed Shaltot4

1Department of Architecture, Cairo University, Egypt; 2Construction Engineering Department, The American University in Cairo, Egypt; 3Construction and Building Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt; 4EGEC Qatar for Engineering Consultations, Doha, Qatar

Aim and Approach

(max 200 words)

While daylighting and thermal comfort design requirements in schools have significantly evolved, their associated metrics are still lagging behind. Most of the well-established relevant literature has long focused on illuminance, Daylight Factor (DF) and thermal charts. Nevertheless, modern Building Information Modeling (BIM)-enabled tools have provided optimal sustainable building design and performance. New metrics have emerged, namely Daylit Area % (DLA), Mean Daylight Factor (µDF), Daylight Autonomy (DA) and Useful Daylight Index (UDI), and unmet temperature and humidity hours. Moreover, there was a lack in previous research on schools in hot arid climate. Thus, this research proposes a BIM-enabled early design multi-objective simulation and optimization model for daylighting and thermal comfort in prototypical school buildings, whose aim is reaching the best combination of Window-to-Wall Ratio “WWR” and building orientation. The model features a Design Database Module (DDM), a Performance Criteria Database (PCD), and an Analytical Hierarchy Process Module (AHPM), used to estimate Criteria Relative Weights (CRW’s). Next, scenarios are generated and checked against the site constraints and building codes, and only valid scenarios are considered using a Scenario Analysis Engine (SAE). For each valid scenario, a Combined Criteria Score (CCS) is obtained, and the optimum scenario is selected by the user.

Scientific Innovation and Relevance

(max 200 words)

While traditionally, school building performance simulation was mostly performed for a few number of performance measures, this paper presents an AHP tool that enables the inclusion of as many measures as possible. This has been demonstrated by the authors through the adoption of five different daylighting metrics and two thermal comfort measures. On another hand, while the daylighting simulation was performed by integrating Rhinoceros® and DIVA® software tools, thermal simulation was carried out by integrating OpenStudio® and EnergyPlus® software packages. AHP criteria weights methods as well as three of the adopted software tools were scarcely used in previous related work.

Preliminary Results and Conclusions

(max 200 words)

For the purpose of model implementation and testing, the authors considered three school building case studies, all situated in hot arid climates: Egypt, Saudi Arabia and Qatar. In general, the daylighting and thermal simulation results tended to provide better CCS results upon increasing WWR, with less dependence on building orientation. While the verification of the AHPM was done using consistency indices and ratios, the validation of the PCD and the model output was performed using surveys filled-in by experts in the field of architectural design. It was concluded that while the model proved as a useful early-design tool for school buildings in hot arid climates, it could well be extended to include other performance metrics such as energy and cost performance, and could also be used with other types of prototypical designs, such as residential, administrative, service buildings retail and congregational buildings.

Main References

(max 200 words)

Anderson, D. R., Sweeny, D. J., Williams, T. A., Camm, J. D., & Martin, R. K. (2014). An Introduction to Management Science: Quantitative Approach to Decision Making (13th ed.). Mason, OH, US: South Western Cengage Learning.

ASHRAE. (2010). ASHRAE Standard 55-2010: Thermal Environment Conditions for Human Occupancy. Atlanta, GA, US: ANSI.

Mahmoud, M. A., Mashaly, I. A., Rashed, Y. M., & Nassar, K. (2016). A new dynamic climate-based daylight metric for sustainable building design in hot climates. Modern Environmental Science and Engineering, 1(2).

Reinhart, C. F. (2014). Daylighting Handbook 1: Fundamentals, Designing with the Sun. (R. Stein, Ed.) United States: Cristopher Reinhart.



14:40 - 14:47

End-use disaggregation in commercial buildings with the building automation system trend data

Narges Zaeri1, H. Burak Gunay1, Araz Ashouri1,2, Ian Wilton2

1Carleton university, Ottawa, Canada; 2National Research Council Canada, Ottawa, Canada

Aim and Approach

(max 200 words)

End-use submetering is essential for energy management in large commercial and institutional buildings. However, most existing buildings lack adequate submetering even for major end-uses. End-use disaggregation techniques offer an untapped opportunity to supplement deficiencies in a metering network. This study presents an end-use disaggregation method for commercial buildings by using building automation system (BAS) data. The BAS trend data provide contextual information about the operational state of major energy-consuming systems and equipment such as fans, pumps, air handling unit (AHU) heating and cooling coils, and chillers. The method applies a series of multiple linear regression models disaggregating bulk metered heating, cooling, and electricity use data into different end-uses by using BAS data as predictors. The results demonstrate that the method can accurately disaggregate hourly building level electricity, heating, and cooling use into their end-use categories.

Scientific Innovation and Relevance

(max 200 words)

In this study, the energy demand modeling procedure using two estimation methods was established by the following steps: (1) pre-processing of raw data, (2) feature selection, (3) model development, and (4) model evaluation and error calculation.

Two regression models were developed for each of the three energy meters: Sci-kit-learn linear-model (Python library) and the genetic algorithm(GA).

This analysis is performed using measured hourly energy consumption data of an academic office building in Ottawa, Canada. Hourly operational data for energy consumption was collected from the meter and automation system as well as WiFi access point data. Unlike most of the previous studies, this disaggregation problem utilized automation systems such as AHU fan state and occupancy estimation as well as meter data for energy reading to train the regression models.

Preliminary Results and Conclusions

(max 200 words)

In this study, linear regression models that disaggregate bulk electricity, cooling, and heating meter data into end-uses of the higher spatial and categorical resolution were developed. The models use BAS trend data that indicate the operational state of the major energy-using equipment (e.g., AHU supply fan state). The models were trained with the Sci-kit-learn and the genetic algorithm. Their accuracy and ability to disaggregate end-uses were demonstrated upon data from an academic office building in Ottawa, Canada.

For electricity, the end-uses considered were fans and pumps, lighting and plug loads, and chillers. For heating and cooling, they were AHU heating and cooling coils, reheat coils, and other perimeters heating devices.

From the results of this study, it was observed that low-frequency meter data could be disaggregated at a reasonable accuracy with provided BAS data.

Main References

(max 200 words)

1.Burak Gunay, H., Z. Shi, I. Wilton, and J. Bursill (2020). Disaggregation of commercial building end uses with automation system data. Energy and Buildings 223, 110222.

2. Sh. Walker, W. Khan, K. Katic, W. Maassen, Wim Zeiler.” Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings”. Energy and Building 209, (2020)

3.W. O’Brian, A. Abdelalim, and H. B Gunay,” Development of an office tenant electricity use model and its application for right-sizing HVAC equipment,” Journal of Building Performance Simulation, vol.12, pp37-55,2019/01/02.

4. Eva Garcia-Martin, Crefeda Faviola Rodrigues, Graham Riley, HakanGrahn. “Estimation of energy consumption in machine learning”.Journal of Parallel and Distributed Computing Volume 134, December 2019, Pages 75-88.

5.C.Fan, Y.Ding, “Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model, Energy Build 19 7 (2019 )7–17,doi: 10.1016 /J.ENBUILD. 2019.05.043



14:47 - 14:54

The impact of using the application of a CNN-based approach for equipment usage detection on building energy

Shuangyu Wei, Paige Wenbin Tien, Yupeng Wu, John Kaiser Calautit

University of Nottingham, United Kingdom

Aim and Approach

(max 200 words)

The present work will develop a coupled approach based on computer vision and deep learning to detect and classify the usage of the equipment in a building space in real-time and concurrently. We propose a technique based on the Faster regions with CNN features (R-CNN) to process the obtained to detect and recognise the usage of equipment in an office space. The model is trained and deployed to a standard camera, and field tests were carried out in an office space. During the field test, the capabilities of the proposed method are evaluated by trying to detect how the occupants interact with personal computers. Different evaluation metrics suggested by previous works were employed. The field tests were carried out in an actual working space in a building at the University. The acquired real-time information is used to generate equipment usage profiles which can then be used to control HVAC operation and as input for building energy simulation (BES) models. In this study, the influence of the coupled approach on the energy demand is evaluated by simulating the case study building in BES with the typical profile and deep learning generated profile.

Scientific Innovation and Relevance

(max 200 words)

As the use of equipment in office buildings increases, accurate equipment usage detection is valuable for energy consumption reduction in the built environment. Using the collected equipment usage information, building energy management system can automatically adjust the operation of heating, ventilation, and air-conditioning systems to meet the actual demands in different conditioned spaces in real-time. Previous studies highlighted that the use of conventional control strategies in office buildings such as “static” operation schedules could cause large energy waste in particular during unoccupied hours. Common equipment load detection techniques, such as power meters and survey, are unable to provide comprehensive and real-time equipment information necessary for demand-driven strategies. Building up from our previous research, a deep learning-based equipment load detection approach which employs artificial intelligence (AI) enabled cameras is proposed to obtain the accurate heat gain prediction and enable HVAC systems to meet the requirement of thermal comfort.

Preliminary Results and Conclusions

(max 200 words)

The experimental results presented a detection accuracy of equipment detection of 83.33%. To investigate the impact of the proposed approach on building energy performance, the case study building was modelled and simulated. The simulation results presented that up to 35.95% reduction of internal heat gains could be potentially achieved with the use of deep learning influenced equipment detection profiles in comparison with the use of static schedules. It highlights the benefits of using real-time deep learning detection method to provide profiles to HVAC systems to achieve demand-driven controls which can minimize unnecessary building energy consumption while maintaining a comfortable indoor environment.

Main References

(max 200 words)

1. Menezes AC, Cripps A, Buswell RA, Wright J, Bouchlaghem D. Estimating the energy consumption and power demand of small power equipment in office buildings. Energy and Buildings. 2014/06/01/ 2014;75:199-209. doi:https://doi.org/10.1016/j.enbuild.2014.02.011

2. Gunay HB, O’Brien W, Beausoleil-Morrison I, Gilani S. Modeling plug-in equipment load patterns in private office spaces. Energy and Buildings. 2016/06/01/ 2016;121:234-249. doi:https://doi.org/10.1016/j.enbuild.2016.03.001

3. Mahdavi A, Tahmasebi F, Kayalar M. Prediction of plug loads in office buildings: Simplified and probabilistic methods. Energy and Buildings. 2016/10/01/ 2016;129:322-329. doi:https://doi.org/10.1016/j.enbuild.2016.08.022

4. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 06/04 2015;39doi:10.1109/TPAMI.2016.2577031



 
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