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: 19th May 2022, 13:36:28 CEST

 
 
Session Overview
Session
Session T3.1: Practice and industry related case studies
Time:
Thursday, 02/Sept/2021:
13:30 - 15:00

Session Chair: Joel Neymark, J. Neymark & Associates
Session Chair: Jacopo Vivian, University of Padua
Location: Cityhall (Belfry) - Room 1

External Resource: Click here to join the livestream. Only registered participants have received the access code for the livestream.
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Presentations
13:30 - 13:48

Using simulation, visualization and monitoring to address barriers to high-performance building strategies

Nathan Brown1, Ibone Santiago1, M Susan Ubbelohde1,2, George Loisos1, Santosh Philip1

1Loisos + Ubbelohde, United States of America; 2University of California, Berkeley

Aim and Approach

(max 200 words)

Design strategies for high performance often require a tuned system where envelope and HVAC strategies are complementary. However, normative approaches tend to encourage the design of oversized HVAC equipment as well as its use independent of envelope systems, thus potentially undermining high-performance strategies. One contributing factor is the use of compliance simulations containing envelope assumptions and behavior that are often not representative of the specificity of high-performance projects, which reduces their usefulness as a design tool. Other factors include risk-aversion of the design team and stakeholder preconceptions of thermal comfort factors as related to HVAC systems and not to the envelope.

This paper presents such methods, developed through practice, to use simulation, visualization, and monitoring as tools to quantify, communicate, and document performance. We present 3 case studies, one in depth, to illustrate the use of these methods to dramatically change the size and concept of mechanical systems. In addition, we present the use of developing a monitoring plan in order to further assuage the fear that HVAC designers will be held accountable for thermal variation outside the control of mechanical systems.

Scientific Innovation and Relevance

(max 200 words)

The transition to a zero-energy, zero-carbon future has to include significant shifts in building practices deployed today. We have much of the knowledge required: much research has been published on innovative approaches to integrating building envelope and mechanical system design. We also have powerful resources around simulation and optimization techniques.

A significant barrier is the entrenchment of normative practice in everyday practice among design teams.

This paper adds to the body of knowledge of how to deploy modeling, visualization and monitoring as pieces of the bridge from normative to high-performance practice.

Preliminary Results and Conclusions

(max 200 words)

We illustrate this approach first through projects to condition classroom spaces using tempered outside air rather than a packaged HVAC system. We then present a detailed case study of an art center/residence designed with high mass and earth-coupled thermal strategies. Initial simulations demonstrated the significant energy reduction benefits of these strategies.

In each of these cases, project particularities emerged as challenges to high-performance, low-energy approaches, including wide variations in occupancy profiles, stakeholder thermostat setting preferences informed by non-representative previous experience, and resiliency scenarios. These challenges required a plan for “who owners will call when something goes wrong.”

Modeling and visualization provided a way to test scenarios being considered and help the team and stakeholders understand performance differences between each scenario. This allowed team members, especially mechanical system designers, to managing expectations and delineating areas of responsibility. Development of a monitoring plan provided a second layer of risk management since it provided a plan to disaggregate potential future issues with performance. In this way, performance issues can be more easily disaggregated as issues related to envelope, mechanical system, or building operation. Monitoring thus formed an additional insurance policy so that thermal performance can be compared to expectations (simulated results).

Main References

(max 200 words)

Levitt, Brendon, et al. "Thermal autonomy as metric and design process." CaGBC National Conference and Expo: Pushing the Boundary–Net Positive Buildings. 2013.

Nytsch-Geusen, C., Kaul, W,. Rädler, J., Westermann, L., Shenoy, V., Balekai, P. (2019). The Digital Twin as a Base for the Design of Building Control Strategies

http://www.ibpsa.org/proceedings/BS2019/BS2019_210389.pdf

Jones, N, L., Chaires, I., Goehring, A. (2019). Detailed Thermal Comfort Analysis from Preliminary to Final Design

http://www.ibpsa.org/proceedings/BS2019/BS2019_210875.pdf

Walikewitz, N., Jänicke, B., Langner, M., Meier, F., Endlicher, W. (2015). The difference between the mean radiant temperature and the air temperature within indoor environments: A case study during summer conditions. Build Environ 84, 151-161.

Raftery, P., C. Duarte, S. Schiavon, and F. Bauman. 2017. A new control strategy for high thermal mass radiant systems. www.escholarship.org/uc/item/5tz4n92b



13:48 - 14:06

In-situ validation of a new sizing methodology for combined production and distribution for domestic hot water and space heating

Senne Van Minnebruggen, Ivan Verhaert

University of Antwerp,Faculty of Applied Engineering, Antwerp, Belgium

Aim and Approach

(max 200 words)

Recent studies show that collective heating and cooling have the potential to decrease CO2-emissions and offer flexibility to increase the amount of renewable energy sources in the overall energy supply [1][2]. In order to achieve these advantages a proper design is crucial. The first step in the design of an optimal collective heating system is sizing. Within Belgian context, currently no standards or design rules exist for the sizing of collective heating systems that provide hot water for space heating purposes as well as for domestic hot water[3]. However, in practice a diversity of ‘customized design rules’ exists. Designers use a mix of foreign design rules and own rules of thumb or fabricant specific design guides. The lack of transparency disables a proper comparison and prevents new insights into either space heating of DHW demand to be incorporated. Eventually, this leads to poorly designed systems and discussions between different stakeholders after installation. This raises the question: ‘How to properly and transparently size collective heating systems?’ By comparing different design methods and analysing heat meter data the objective of this paper is to validate a recently introduced design rule [3], which tackles the issue of transparency and compatibility with other standards.

Scientific Innovation and Relevance

(max 200 words)

In order to validate this new design rule a validation methodology has been developed to define the peak heat consumption of the installation on the basis of data from heat meters. Subsequently, the methodology determines a characteristic that shows the possible combinations of production capacity and heat storage to cover the peak heat consumption. However, some challenges arise in order to analyse the peak heat consumption. Firstly, the poor quality of the data from heat meters needs to be addressed. To cope with this a datacleaning is performed. Secondly, to obtain the peak heat consumption one has to take the influence of the outdoor conditions and the data resolution (measurement interval size) into account. In order to deal with the data resolution the methodology uses insights from a recent design methodology for DHW systems based on tap patterns[4]. Furthermore, to cope with the influence of the outdoor conditions, the relation between the peak heat consumption and the outdoor temperature is investigated. As a result the maximum or worst case peak heat consumption profile can be determined and, consequently, the corresponding production capacity – heat storage characteristic. The outcome of the design rule can then be tested based on this characteristic.

Preliminary Results and Conclusions

(max 200 words)

In order to test the methodology and consequently validate the design rule, the validation methodology was applied to two case studies. From the result of the case studies follows that the method is able to obtain the desired characteristic. Nevertheless, the design rule could not be validated. Based on the analysis of the heat consumption of both case studies over the considered measurement period, some deviations in heat consumption were found. As a result of these deviations, the relationship between peak heat consumption and outdoor conditions could not be determined unequivocally. It is therefore possible that the result obtained underestimates the required capacity. In order to validate the design rule in a representative manner and to refine the validation methodology, it is advisable to apply the validation methodology to several case studies. Whereas these case studies should meet some requirements/advices as set out in this paper. Also, due to the diversity in design rules, an overview and evaluation of these different design rules proves to be necessary.

Main References

(max 200 words)

[1] D. Connolly et al., “Heat Roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system,” Energy Policy, vol. 65, pp. 475–489, Feb. 2014, doi: 10.1016/J.ENPOL.2013.10.035.

[2] H. Lund et al., “4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems,” Energy, vol. 68, pp. 1–11, Apr. 2014, doi: 10.1016/J.ENERGY.2014.02.089.

[3] I. Verhaert, “Design methodology for combined production and distribution for domestic hot water and space heating,” E3S Web Conf., vol. 111, p. 01089, 2019, doi: 10.1051/e3sconf/201911101089.

[4] I. Verhaert, B. Bleys, S. Binnemans, and E. Janssen, “A Methodology to Design Domestic Hot Water Production Systems Based on Tap Patterns,” CLIMA2016 - Proc. 12th REHVA World Congr., 2016.



14:06 - 14:24

Comparison of Heating Power calculated through standard and building simulation models

Paul Van den Bossche1, Jeroen Van der Veken1, Sébastien Pecceu1, Stijn Verbeke2

1BBRI, Belgium; 2EMIB, UAntwerpen, Belgium

Aim and Approach

(max 200 words)

With the shifting focus from increasing renewable energy production towards energy distribution issues and finding a continuous match between supply and demand, the need to define a correct required heating power in buildings also increases. Whereas calculations in the past mainly focused on comfort and ‘enough power at any moment’, a better understanding of the profiles of the required heating power is necessary to bring it in line with renewable energy availability. In addition, the installation costs of highly efficient systems such as heat pumps are more dependent on the installed power compared with classic boiler systems. However, experience from practice tend to show that the EN12831-1 overestimates the necessary heating power.

Scientific Innovation and Relevance

(max 200 words)

Key Innovations

• Comparison of calculated, measured and simulated heat power for about 1000 cases

• Simulations based on realistic winter conditions

Research Implications

The original climatic data (period 2010-2020) showed clear correlations between dry bulb temperature, wind speed and solar radiation, all impacting the heat balance. When studying dynamics at extreme (cold) periods, these correlations need to be maintained, while commercially available climatic data the air temperatures are often not correlated with the wind data.

Preliminary Results and Conclusions

(max 200 words)

Both measurements and simulations show an overdimensioning factor that ranges from rather small and acceptable (10 %, a minimum safety margin that could be justified for sizing purposes) to very significant (> 100 %), which is probably not acceptable from economic and efficiency related perspective.

What we can conclude from the simulation results is that between -3°C and 15°C the required power follows the trend of the linear heating curve that connects the calculated standard power at design temperature and 0 at 20°C. However, below these temperatures the simulated retrieved power appears to flatten out; thereby increasing the discrepancy with the calculations according to the procedures in the standard.

Several explanations may be at the origin of this effect. Firstly, in the Belgian climate no days are observed with, at the same time, the lowest temperatures, maximum wind speed and without solar gains. The standard implicitly assumes simultaneous occurrence of these factors, but this is not observed in the actual weather data.

Looking at our measurement data, this ‘plateau effect’ is even increased, probably by changing occupant behaviour and automatic control interventions.

Main References

(max 200 words)

CEN - European Committee for standardization (2017) EN 12831-1 : Energy performance of buildings - Method for calculation of the design heat load - Part 1: Space heating load (EN 12831-1:2017)

Dols, W. S., & Polidoro, B. J. (2015). Contam user guide and program documentation version 3.2 (No. Technical Note (NIST TN)-1887).

EnergyPlus version 9.4.0; Lawrence Berkeley National Laboratory, U.S. Department of Energy and others.

NBN - Bureau voor normalisatie (2020). NBN EN 12831-1 ANB:2020. Belgian annexe to EN 12831-1:2017, in Dutch and French.

Peccue, S., Van den Bossche, P., Impact of Building Airtightness on Heat Generator and Heat Emission Equipment Sizing. AIVC Conference Athens September (2020 postponed) 2021.

Verbeke, S., Thermal inertia in dwellings, PhD dissertation, Universiteit Antwerpen (2017)



14:24 - 14:42

Definition and validation of a thermal zone model in Modelica language with Holzkirchen Twin Houses experiment

Rossella Alesci1, Ettore Zanetti1, Rossano Scoccia1, Alberto Leva2, Mario Motta1

1Department of Energy, Politecnico di Milano, 20156 Milano, Italy; 2Dipartimento di Elettronica, Informazione e Bioingegneria , Politecnico di Milano, 20133 Milano, Italy

Aim and Approach

(max 200 words)

This manuscript presents the validation of the Modelica library “BuildingsAndPlants” thermal zone model. The Twin Houses experimental data and a TRNSYS model [1] were used as comparison. The “BuildingsAndPlants” library allows to easily integrate new models representing new advanced technologies (such as ventilated façades, double skin façades etc.) or new HVAC systems models in thermal zone model. In fact, until now, this process is difficult and time-consuming using the already available software.

In the “BuildingsAndPlants” library the use of components from the Modelica Standard library is reduced and all the heat transfer and fluid dynamics are modelled inside the library. In addition, it tends to minimize the use of inheritances and other properties that allow to write the code in a slightly more redundant but simpler way making it easier for the users to retrace the equations at the base of the models.

Scientific Innovation and Relevance

(max 200 words)

This work focuses attention on providing a modular thermal zone model where each element can be changed. This allows to test different models (model classes, level of physic complexity) and to choose each time the most appropriate for the case under exam. Hence, the scientific innovation of this work is related to the different focus of the "BuildingsandPlants" library.

The thermal zone model defined in the “BuildingsandPlants” library can be adopted as baseline for the analysis of the behavior of new components such as new construction technologies or new HVAC systems. In fact, thanks to the modularity and the understandability of this library it will be very easy for an expert in building physics, to incorporate new components in the existing thermal zone model. In this way, each user can just add the new sub model which he wants to study, without the need to model each time the entire thermal zone from the beginning.

In addition, the understandability of this library will reduce the time required to learn how to use and modify it.

Preliminary Results and Conclusions

(max 200 words)

The preliminary results of the thermal zone model described in this paper are obtained applying the case study of the Twin Houses in Holzkirchen [2].

In particular, in free floating conditions the air temperature trend is close to the one registered by the sensor, with a maximum temperature difference of 1°C and a time shift between the two curves that is almost zero along the entire simulation period.

On the other hand, at constant temperature, the required thermal power is underestimated of the 46% with respect to the value provided by the sensor.

Thus, one may conclude that, in free floating conditions, the model simulates in the correct way the behavior of the reference building, also at a dynamic level, while, when the electric heater is switched on, there is an underestimation of the required thermal power that is related to the hypothesis of full mix on which the model is based. This conclusion is also confirmed by another article [3] which analyzed the same case study.

In addition, the same model was created in TRNSYS and adopted to compare the results. In this case, the difference between the two integrals of the thermal power is of the 10%.

Main References

(max 200 words)

[1] Solar energy laboratory, “TRNSYS 18 Getting Started,” Trnsys 18, vol. 1, pp. 1–9, 2014.

[2] P. Strachan, I. Heusler, M. Kersken, and M. J. Jiménez, “Empirical Whole Model Validation Modelling Specification: Test Case Twin_House_Experiment_2 IEA ECB Annex 58 Validation of Building Energy Simulation Tools Subtask 4.”

[3] G. Masy et al., “Lessons learned from heat balance analysis for holzkirchen twin houses experiment,” Energy Procedia, vol. 78, pp. 3270–3275, 2015.

[4] W. Li et al., “Modeling urban building energy use: A review of modeling approaches and procedures,” Energy, vol. 141, pp. 2445–2457, Dec. 2017.

[5] ASHRAE, “FUNDAMENTALS,” in ASHRAE HANDBOOK, Atlanta,GA, 2013.

[6] A. Sodja and B. Zupančič, “Modelling thermal processes in buildings using an object-oriented approach and Modelica,” Simul. Model. Pract. Theory, vol. 17, no. 6, pp. 1143–1159, 2009.

[7] P. Fritzson, Principles of Object Oriented Modeling and Simulation with Modelica 3.3. IEE Press, Wiley, 2015.

[8] A. Dama and F. Lastaria, “Explicit versus implicit method for radiative heat transfer in gray and diffuse enclosures,” Int. J. Heat Mass Transf., vol. 55, no. 13–14, pp. 3829–3833, 2012.

[9] M. Wetter, “Modeling of Heat Transfer in Rooms in the Modelica ‘ Buildings ’ Library,” 2013.



14:42 - 15:00

Calibration of a multi-residential building energy model – Part II: Calibration using surrogate-based optimization

Van Long Lê1, Charlotte Marguerite1, Charlotte Beauthier1, Olivier Fontaine de Ghélin1, Cécile Goffaux1, Loïc de Moffarts2

1Cenaero, Belgium; 2Thomas & Piron Group, Belgium

Aim and Approach

(max 200 words)

The present study, co funded by European Regional Development Fund and Thomas & Piron Group, aims at developing an energy toolbox for building energy load prediction and building fault detection and diagnosis. The idea is to use a calibrated energy model as an assistant to guarantee the energy performance of the building.

The research approach adopted in the present study is a stepwise energy model calibration. The building energy model is initialized based on the data from design and construction phases of the building and using the OpenStudio/EnergyPlus software. This energy model is then subjected to a sensitivity analysis (details of methodology will be described in a separated paper, i.e. Calibration of a multi-residential building energy model PartI: Cluster-Based sensitivity analysis) with the aim of identifying the most important parameters for the energy consumption. These parameters will then be considered as variables of an optimization-based calibration. A metamodel-based evolutionary algorithm is used to minimize the discrepancies between simulation results and real consumption data collected from smart meters. The Normalized Mean Bias Error (NMBE) and the Coefficient of Variation of the Root Mean Square Error (CvRMSE) will be calculated for each apartment to assess the accuracy of calibrated energy model.

Scientific Innovation and Relevance

(max 200 words)

The present study deals with the calibration of a white-box energy model of a multi-family, multi-story building. The latter has 40 apartments having 1 to 3 bedrooms and a single-family house with 3 bedrooms. The energy consumption data are collected by smart meters installed for each building apartment. The real weather data are provided by the Royal Meteorological Institute of Belgium. As the calibrated energy model will be used to predict energy load as well as for fault detection and diagnosis at building and apartment levels, the energy model considers a large number of parameters. Even after a reduction step (selection of the most influent parameters through a sensitivity analysis), the total number of parameters for the optimization-based calibration is still large. Moreover, the white-box energy model simulation is quite expensive in terms of computational time. Therefore, the metamodel technique (e.g. Radial Basis Function, Kriging) is used for reducing computational costs of the white-box energy model simulation and the evolutionary algorithm, implemented within the Cenaero inhouse optimization platform, is used for finding the global optimum of the constrained optimization problem.

Preliminary Results and Conclusions

(max 200 words)

The preliminary results show that the accuracy of energy model is largely improved compared with the baseline energy model of the building. The NMBE values calculated are within the recommended value range, which indicates that the discrepancies between the simulation results and the measurements are reasonable. However the CvRMSE is still quite high for several apartments and additional work is needed to obtain a value that fits better to the recommendations for model calibration (e.g. within the range validated by a performance measurement and verification protocol such as the ASHRAE guideline 14, International Performance Measurement and Verification Protocol).

Main References

(max 200 words)

Crawley, Drury B., Linda K. Lawrie, Frederick C. Winkelmann, W. F. Buhl, Y. Joe Huang, Curtis O. Pedersen, Richard K. Strand, et al. 2001. “EnergyPlus: Creating a New-Generation Building Energy Simulation Program.” Energy and Buildings. https://doi.org/10.1016/S0378-7788(00)00114-6.

Beauthier, Charlotte, Paul Beaucaire, Caroline Sainvitu, 2017. “A Surrogate-Based Evolutionary Algorithm for Highly Constrained Design Problems.” In GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1614–21. New York, NY, USA: Association for Computing Machinery, Inc. https://doi.org/10.1145/3067695.3082538.

Li, Wancheng, Zhe Tian, Yakai Lu, and Fawei Fu. 2018. “Stepwise Calibration for Residential Building Thermal Performance Model Using Hourly Heat Consumption Data.” Energy and Buildings. https://doi.org/10.1016/j.enbuild.2018.10.001.

Fabrizio, Enrico, and Valentina Monetti. 2015. “Methodologies and Advancements in the Calibration of Building Energy Models.” Energies. https://doi.org/10.3390/en8042548.

Sun, Kaiyu, Tianzhen Hong, Sarah C. Taylor-Lange, and Mary Ann Piette. 2016. “A Pattern-Based Automated Approach to Building Energy Model Calibration.” Applied Energy 165 (March): 214–24. https://doi.org/10.1016/j.apenergy.2015.12.026.

Goldwasser, David, Brian Ball, Amanda Farthing, Stephen Frank, and Piljae Im. 2018. Advances in Calibration of Building Energy Models to Time Series Data: Preprint. Golden, CO: National Renewable Energy Laboratory. NREL/CP-5500-70865.



 
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