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: 6th July 2022, 15:10:32 CEST

 
 
Session Overview
Session
Session F1.2: Ensuring high quality building simulations
Time:
Friday, 03/Sept/2021:
8:30 - 10:00

Session Chair: Andrea Gasparella, Free University of Bozen - Bolzano
Session Chair: Aurelien Bres, AIT Austrian Institute of Technology
Location: Cityhall (Belfry) - Room 2

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Presentations
8:30 - 8:48

Effect of reduced air change rates on indoor air quality and air conditioning energy consumption in retail buildings

Konstantin Finkbeiner, Martin Kremer, Martin Rätz, Xuchao Ying, Paul Mathis, Dirk Müller

RWTH Aachen University, Germany

Aim and Approach

(max 200 words)

In the studies of [Mathis] and [Finkbeiner] the energy saving potential was investigated by measures such as reducing the air exchange rate in sales rooms and simultaneously increasing the water temperature of the refrigeration circuit. Savings of up to 35 % were proven

in the overall system.

Although the measures examined appear to be very promising, the condensation of water from moist air cannot be expected to be unproblematic. The reduction in fresh air volumes implies a reduced removal of moisture caused in shopping centers, especially by people. This can lead to an uncomfortable relative humidity level, even if the supply air humidity is controlled by an AHU.

This paper examines the effect of reduced air exchange rates on indoor humidity, assuming that only persons are the source of moisture. The minimum limit of the air exchange rate determined in [Mathis] is examined with regard to the aspect of comfortable indoor air humidity and, if necessary, further restricted.

For this purpose, the building model already presented in [Finkbeiner] is extended to include balancing of room air humidity. Moisture loads by people are implemented. In addition, the setting of the supply air humidity of the AHU is included in the analysis.

Scientific Innovation and Relevance

(max 200 words)

The low-order building model of [Lauster] is validated and is used in the context of simulative investigations of the energy demand of buildings. The model was primarily developed for the investigation of thermal energy demand of city districts and shows comparatively high simulation speed. Nevertheless, the accuracy of the simulation results is satisfactory. In the context of the consideration of city districts, the heat demand is the focus of the investigations. Thus, the building model of[Lauster] is also primarily intended for the sensitive change of state of the indoor air. Condensation effects, which mainly occur when using air water systems for indoor room

conditioning, are not considered. In this paper, the building model of[Lauster] is extended to include the consideration of moist indoor air in the energy demand calculation. For this purpose, the room air node is extended by the balancing of the water content in the moist air. On the other hand, the model of the air water systems is supplemented by balancing the water content of the humid air, so that the additional energy consumption that results from condensation during indoor air cooling can be taken into account.

Preliminary Results and Conclusions

(max 200 words)

The model of large retail buildings, especially shopping centres, presented by[Finkbeiner] was further calibrated on the basis of more detailed monitoring data, so that the accuracy of results could be further increased, as the simulation results shown in the attachment show. The simulated electrical demand of the monitored shopping center was primarily determined by converting the thermal demand. Looking at a period of six months, a deviation of 2% can be observed. A comparison of the simulated interior temperature with the measured one also shows a comparatively small deviation, see attachment. The water content of moist air indoors is largely determined by room air temperature. Thus, the calibrated model offers a good prerequisite for extending the air node model to include water content balancing of the moist indoor air. In addition, the model presented by[Finkbeiner] already takes into account the water content of the

outside air, the control of the supply air humidity via the AHU and the surface temperature of the air water systems.

Main References

(max 200 words)

[Finkbeiner] K. Finkbeiner, P. Mathis, F. Hintz, E. Bykhovskaya, L. Engelmeyer, D. Bohne and D. Müller, “Modeling a Building Energy System for Development of Energy Efficient Systems of Shopping Centers,” in Proceedings of the 15th IBPSA Conference, San Francisco, USA, 2017.

[Mathis] P. Mathis, H. Freitag, D. Hegemann, M. Schmidt and D. Müller, “New Concepts for the Ventilation of Shopping Centers: Reducing Air Change Rate, Applying Active Chilled Beams and Elevating Cold Water Supply Temperature,” Indoor and Built Environment, vol. 26, no. 2, pp. 08-225, 2017.

[Lauster] M. Lauster, J. Teichmann, M. Fuchs, R. Streblow and D. Müller, “Low order thermal network models for dynamic simulations of buildings on city district scale,” Building and Environment, p. 223–231, 2014.



8:48 - 9:06

Metrics for evaluating envelope performance in next generation energy codes

Rasoul Asaee1, Adam Wills2, Alex Ferguson1

1Natural Resources Canada, Canada; 2National Research Council, Canada

Aim and Approach

(max 200 words)

A performance path, which is a common approach for compliance in building codes, requires the whole building to perform up to a minimum limit. The performance path provides an opportunity for builders to use innovative design and technologies. This study aims to create criteria to capture the contribution of the building fabric and airtightness to the energy-efficient operation of the building while excluding the effects of mechanical equipment (e.g., space heating system type and ventilation system) and associated gains (e.g., standby losses of hot water tank). The metric should find a balance between the complexity of calculation and accuracy of output. Energy codes should not be dependant on building performance simulation software, and the building designers should be able to calculate the metric using a wide range of software options. Auhtors developed multiple metrics to assess the performance of envelope design and evaluated the impact of those metrics on building design in several climate zones.

Scientific Innovation and Relevance

(max 200 words)

Criteria for envelope improvement are considered desirable because these elements of a building are less frequently replaced and upgraded during the life of the building and can actively reduce the heat demand, whatever equipment is subsequently installed. Energy efficiency savings that must be achieved through envelope improvements alone (airtightness, insulation, glazing, solar design), and demonstrated through building performance modeling. The addition of a building envelope metric to the energy codes prevent compliance with the performance target by relying solely on a heat pump technology.

Preliminary Results and Conclusions

(max 200 words)

This paper presents the results obtained from the impact analysis of the proposed performance target in the new building code. The authors determined whether the new requirements set forward for the envelope metric can be achieved with technologies and approaches that builders have previously used in low-energy homes. We evaluated the energy savings and capital cost impact for achieving the envelope targets in different housing types and climate zones. Results indicate that the envelope metrics prioritizes investments in insulation, air-sealing, and energy-efficient windows. This philosophy presumes that cost savings can be found by reducing heating loads, thereby permitting the use of smaller and simpler heating equipment.

Main References

(max 200 words)

Government of British Columbia, "Energy Step Code - Building Beyond the Standard," 2019. [Online]. Available: https://energystepcode.ca/.

Natural Resources Canada, "Lessons learned and key findings from the ecoEII Net-Zero Demonstration and the R-2000 Net-Zero Energy Pilot," 2019.

BC Housing, "Energy Step Code 2017 Metrics Research," 2017. [Online]. Available: https://www.bchousing.org/research-centre/library/residential-design-construction/energy-step-code-2017-full-report&sortType=sortByDate.



9:06 - 9:24

Developments in simulation and evaluation of large-scale hot water tanks and pits in renewables-based district heating

Abdulrahman Dahash, Fabian Ochs, Alice Tosatto

University of Innsbruck, Austria

Aim and Approach

(max 200 words)

The efforts to phase-out the conventional fuels and achieve the decarbonization of the heating sector are notably increasing. Thus, the integration of renewable energy resources (e.g. solar energy, geothermal) found its place favorably in district heating (DH) whereby the share of renewables is gradually increasing in R-DH systems [1]. Yet, renewables integration can somehow alter the energy scheme and the operation leading to shortcomings (e.g. security of supply not met) [2]. Consequently, large-scale thermal energy storage (TES) is advantageous to bridge the gap between the renewables’ availability and heat demand eliminating the mismatching.

Accordingly, it is held that a large-scale and long-term TES enables a more flexible integration of renewables in DH systems and, as a result, has benefits in terms of lower fossils consumption, higher primary energy savings and lower CO2 emissions. Yet, the high capital cost associated to STES is frequently a major downside. Together with the space availability, complex planning layout and the presence of groundwater tables, these are the set of major challenges in STES domain to be tackled among others [2]. Thus, simulation-based analyses found its place favorably in the planning phase of such large-scale systems since its planning is considered a complex process.

Scientific Innovation and Relevance

(max 200 words)

To select properly the design, geometry and construction type for a large-scale TES, a number of inputs (hydro- geological factors, system characteristics, thermal losses, investment cost, etc.) are repetitively evaluated until a compromise between the technical performance and the economic feasibility is found [2]. Therefore, modeling is ideally suited to tackle the different challenges during planning to produce the optimal layout for the chosen TES.

Therefore, this work reports the development of large-scale TES numerical models in the framework of an international project entitled “Giga-Scale Thermal Energy Storage for Renewable Districts” [3] addressing the limitations, downsides and advantages based on experience in order to transfer the knowledge for other researchers and planners. Further, the work provides the major modeling parameters and their influence on TES operation and answers relevant questions such as to what extent and accuracy it is required to include these parameters. Moreover, the work compares the functionality of different techno-economic measures and addresses its applicability for large-scale TES. Such measures are energy efficiency, TES capacity efficiency, stratification and MIX numbers, thermocline thickness and rate of entropy generation.

Preliminary Results and Conclusions

(max 200 words)

In the modeling process hierarchy for the TES modeling, a pre-design tool is crucial for the early TES planning to examine TES potential. Next, the details are systematically included in the model for a detailed design. Later, it is necessary to examine the functionality of the integrated TES into a specific energy system and, herein, technology integration tools are required. As a result, the technology is thoroughly evaluated and the outputs are post-processed in the evaluation level. If the planners are not yet satisfied with the results, then TES planning reaches to the optimization level whereby different players (e.g. construction type, insulation thickness) are examined.

Within “Giga-Scale Thermal Energy Storage for Renewable Districts”, also co-simulations scenarios were proposed for large-scale TES. Thereby, a co-simulation scheme between a detailed design tool (i.e. COMSOL Multiphysics) and technology integration tool (i.e. Modelica/Dymola) was also tested and the results demonstrated the applicability of this approach. Yet, the feasibility of this method was limited due to the required computation efforts.

Main References

(max 200 words)

[1] Ochs et al., "Techno-economic planning and construction of cost-effective large-scale hot water thermal energy storage for Renewable District heating systems," Renewable Energy, vol. 150, pp. 1165-1177, 2019.

[2] Dahash et al., "Advances in seasonal thermal energy storage for solar district heating applications: A critical review on large-scale hot-water tank and pit thermal energy storage systems," Applied Energy, vol. 239, pp. 296-315, 2019.

[3] Giga_TES, “Giga-Scale Thermal Energy Storage for Renewable Districts”, https://www.gigates.at/, Flagship project (FFG, Energieforschung), 2019.



9:24 - 9:42

Energy flexibility quantification with integrated heat pump, floor heating system, and thermal storage in a school building

Navid Morovat1, José Agustín Candanedo1,2, Andreas K Athienitis1

1Concordia University, Montreal, Canada; 2CanmetENERGY, Montreal, Canada

Aim and Approach

(max 200 words)

School buildings are an important part of the building stock; they also represent a sizeable portion of the total energy use in the building sector. However, HVAC systems in schools are often non-optimized in terms of energy consumption and efficiency, and therefore, the energy cost of educational buildings is considerable. Thus, satisfying the increasing requirements of environmental performance standards –along with the need to enhance energy flexibility– is a challenging task[1-4].

While simulations have often been used to investigate energy consumption patterns in schools [5], there are relatively few publications on measured energy use in schools in general [6,7] and even fewer publications on new/low-energy schools. Considering that many schools need to be built in the near future, it is imperative to address this gap in data-based research on the energy performance and flexibility of new schools.

In this paper, an iterative calibration process is performed to evaluate the quality of grey-box models and identified parameters in a thermal zone with a floor heating system. The predictions from the model are then compared to experimental data and validated models are used to further study energy performance and energy flexibility of the thermal zone with floor heating in a school.

Scientific Innovation and Relevance

(max 200 words)

The case study school has been in operation since 2017, and it is a 100 % electric building. Features of the school include a high-temperature thermal storage device, local water-to-air heat pumps, and a water-to-water heat pump used for radiant floor heating. There are several sources of high-resolution data, including the BAS and dedicated electrical sub-meters.

This paper focuses on the evaluation of the different control strategies aimed at reducing peak demand and quantifying the energy flexibility potential of the thermal zone. The developed models are based on a data-driven grey-box model formulation and calibrated with measured data which is suitable for application in the context of demand-side management in Smart Grids.

Energy flexibility is an essential part of the solution to address the electrical grid’s challenges such as providing a contingency reserve to be used for emergencies. To address this need, real-time energy flexibility is needed to be predicted ahead of time or calculated continuously and available at short notice (e.g. 10 minutes) over a time duration of a few hours. Using the calibrated model, this paper investigates the application of a Building Energy Flexibility Index (BEFI) to quantifying real-time energy flexibility relative to a reference as-usual energy consumption profile.

Preliminary Results and Conclusions

(max 200 words)

The developed grey-box model shows acceptable accuracy for both the total and instantaneous heat demand in the building, making it suitable for the development of demand-side management control strategies in a Smart Grid context. This study also investigates the calibration of data-driven grey-box models for simulation of energy demand and energy flexibility of a thermal zone with floor heating in the school building.

This paper develops a methodology consisting of four steps: (1) modeling and calibration with experimental data, (2) flexibility characterization, (3) scenario modeling to assess the impact on electricity demand, and (4) evaluating the application of Building Energy Flexibility Index (BEFI) to quantifying real-time energy flexibility and enable interaction between building, aggregators and utilities.

Main References

(max 200 words)

1. Finck, C., R. Li, and W. Zeiler, Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration. Applied Energy, 2020. 263: p. 114671.

2. Hu, M., et al., Price-responsive model predictive control of floor heating systems for demand response using building thermal mass. Applied Thermal Engineering, 2019. 153: p. 316-329.

3. Jensen, S.Ø., et al., IEA EBC annex 67 energy flexible buildings. Energy and Buildings, 2017. 155: p. 25-34.

4. Reynders, G., et al., Energy flexible buildings: An evaluation of definitions and quantification methodologies applied to thermal storage. Energy and Buildings, 2018. 166: p. 372-390.

5. Dasgupta, A., A. Prodromou, and D. Mumovic, Operational versus designed performance of low carbon schools in England: Bridging a credibility gap. HVAC&R Research, 2012. 18(1-2): p. 37-50.

6. Van Dronkelaar, C., et al., A review of the energy performance gap and its underlying causes in non-domestic buildings. Frontiers in Mechanical Engineering, 2016. 1: p. 17.

7. Golshan, M., H. Thoen, and W. Zeiler, Dutch sustainable schools towards energy positive. Journal of Building Engineering, 2018. 19: p. 161-171.



9:42 - 10:00

Integrating energy systems into building design with Hive: Features, user survey and comparison with Ladybug and Honeybee tools

Christoph Waibel, Daren Thomas, Amr Elesawy, Illias Hischier, Linus Walker, Arno Schlüter

Chair of Architecture and Building Systems, ETH Zurich, Switzerland

Aim and Approach

(max 200 words)

Synergistic effects between the energy demand and supply side suggest that building energy systems should be treated as integral parts in the design of buildings (Ferrara et al. 2019, Waibel et al. 2019). In architectural practice and education, however, building systems are commonly regarded as to be specified by (HVAC) engineers to fit a given architectural design. To close this gap, we present a decision support tool that makes the most relevant aspects related to energy systems easily accessible to architects at a conceptual design stage. We discuss results from qualitative studies and semi-structured interviews with architecture students on their experience with performance-driven design and on their experience with the tool. From these surveys, we can identify common challenges that arise in an increasingly complex and technology driven architectural design process.

Scientific Innovation and Relevance

(max 200 words)

This study includes contributions in model development (decision support tool), as well as in empirical research (interviews and questionnaires). The decision support tool is implemented as a plug-in in Rhinoceros Grasshopper. The major innovation of the tool is its flexible software architecture that allows coupling to various third party and in-house model libraries. This framework is realized by establishing clean input-output relations with the introduction of a “distributor” component that processes all inputs into appropriate data types for the calculation models used. All results are then streamed back together and collided into a central results viewer. Thus, it leads to an easily accessible toolset with low entrance barriers, allowing an intuitive integration of energy systems into architectural design - a major difference to other existing workflows and tools. The empirical research with the architecture students is also adding novel findings to the building simulation research community, as it identifies real-world challenges that arise when integrating energy systems into architectural design processes.

Preliminary Results and Conclusions

(max 200 words)

First user tests of the new energy systems integrated design tool with architecture students have shown that the accessibility of novel decision support tools significantly impact design decisions made by architects. To foster the uptake of such tools, we found that (i) providing default values (such as for simulation model settings or technology parameters) but with the option to set custom values, (ii) visual representations (e.g. showing energy systems in 3D as part of the building design), (iii) as well as immediate performance feedback (e.g. implications of architectural design elements such as form, orientation or construction on energy systems design, cost and carbon emissions) are most vital. Finally, due to the growing complexity of architectural design processes with an increasing degree of specialized domain expertise becoming relevant, it is indispensable to provide a design framework that allows flexible compilation of third party model libraries packaged into one coherent architecture-focused design workflow.

Main References

(max 200 words)

M. Ferrara, F. Prunotto, A. Rolfo, E. Fabrizio, Energy demand and supply simultaneous optimization to design a nearly zero-energy house, Appl. Sci. 9 (2019) 2261, https://doi.org/10.3390/app9112261.

C. Waibel, R. Evins, J. Carmeliet, Co-simulation and optimization of building geometry and multi-energy systems: interdependencies in energy supply, energy demand and solar potentials, Appl. Energy 242 (2019) 1661–1682, https://doi.org/10.1016/j.apenergy.2019.03.177.



 
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