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, 14:53:12 CEST

 
 
Session Overview
Session
Session T2.7 (Online Track): Ensuring high quality building simulations
Time:
Thursday, 02/Sept/2021:
10:30 - 12:00

Session Chair: Paul Strachan, University of Strathclyde
Location: Virtual Meeting Room 1

External Resource: Click here to join the Zoom Meeting
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Presentations
10:30 - 10:48

Natural ventilation simulation for application to environmental planning in early design stage

Nozomu Ota, Sei Ito

Shimizu Corpration, Japan

Aim and Approach

(max 200 words)

The performance of natural ventilation varies depending on the local characteristics, surrounding buildings, building shape, ventilation openings arrangement and method of opening and closing, etc., so time and effort are required to determine the energy efficiency effect. Normally it is necessary to quantitatively determine the performance in the early design stage when the building form is determined, but there have been almost no tools that can be used within the limited time and resources of the early design stage. For example, OpenStudio, TRNFlow, and IES are capable of advanced simulation, including natural ventilation, but these are for precise analysis in the latter stages of design and are difficult to use in the early stage.

Therefore coupled indoor-outdoor natural ventilation simulation that enables environmental designers to easily and rapidly investigate energy efficiency schemes has been developed for application to early design stage natural ventilation schemes. The tool has been developed into 3 phases that can be utilized in stages as the design progresses. Phase 1 is site environment analysis, Phase 2 is an examination of multiple proposals, and Phase 3 calculates the annual energy efficiency.

Scientific Innovation and Relevance

(max 200 words)

A Rhinoceros 3D model and wind pressure coefficients obtained from CFD analysis are coupled to the data in each phase, enabling seamless investigation reflecting the site properties from the early design stage.

・In Phase 1, the outdoor airflow analysis is performed considering the surrounding buildings by coupling with CFD software. Natural Ventilation Potential map indicating the openings positions to promote natural ventilation are produced by weighting the wind pressure coefficients with wind direction frequency, and This is the output that gives notice to the environmental designer.

・In Phase 2, multiple or optimization analysis using evaluation indexes by air conditioning reduction time is performed with a tool produced using Grasshopper.

・In Phase 3, the energy efficiency effect is calculated using Visual NETS, a unique Thermal and airflow network simulation program. A ventilation automatic opening and closing function is provided as standard to enable designers to easily and accurately calculate the energy efficiency effect. Also, By minimizing the amount of calculation, adopting the skyline method for solving simultaneous equations, and using the binary output of the result, Visual NETS has achieved 20 times speed than EnergyPlus in the model of 400 rooms. The tool has been widely applied to large-scale buildings.

Preliminary Results and Conclusions

(max 200 words)

From a comparison between actual measurements and simulation for a 9,000 m2 total floor area office building in Tokyo, it was confirmed that temperatures and ventilation rates were generally well reproduced.

Also, a case study was performed for a medium-scale office building, which demonstrated the effectiveness of the simulation. As a result of preparing the potential map in Phase 1 and analyzing multiple proposals in Phase 2, a strong correlation was found between the average wind pressure coefficient of the natural ventilation potential map and the air conditioning reduction time, which confirmed the effectiveness of the natural ventilation potential map. Also in Phase 3, the optimized ventilation opening arrangement increased the cooling load reduction effect by more than 10% compared with an orthodox arrangement of ventilation openings. This suggests that investigating multiple proposals in the early design stage when the ventilation opening scheme can be changed comparatively easily contributes to increasing the energy efficiency effect, and it also confirmed the effectiveness of this simulation.

Main References

(max 200 words)

・Marco Picco, Marco Marengo1(2019) A Fast Response Performance Simulation Screening Tool in Support Of Early Stage Building Design, Building Simulation 2019, Roma Italy.

・Nari Yoon, Ali Malkawi (2017) Predicting the Effectiveness of Wind-Driven Natural Ventilation Strategy For Interactive Building Design, BuildingSimulation 2017, San Francisco.

・Ali Mohammadzadeh, Miroslava Kavgic, Ali Al-janabi (2019) Energy Management System (EMS): The Impact of Natural Ventilation and Shading Controlon Thermal Performance of University Building in Winnipeg, Canada, Building Simulation 2019, Roma Italy.

・H. Okuyama,(1999) Thermal and airflow network simulation program NETS, Building Simulation’99, Kyoto, Japan, pp. 1237–44

・Sei Ito, Yasunori Akashi, Jongyeon Lim (2017)Study on Thermal Load Calculation for Ceiling Radiant Cooling Panel System, Building Simulation 2017, San Francisco.

・Y.Miyakawa, A.Matsuda, T.Kato(2001) Parallel Processing of Space Cholesky Factorization by Generalized Skyline Method, Information Processing Society of Japan, vol. 42, No. 4, pp. 762–770



10:48 - 11:06

Control logic and parameters in a VAV system considering unevenly distributed internal loads and damper characteristics

Akari Nomura1, Shin Yamamoto2, Shohei Miyata1, Yasunori Akashi1, Masashi Momota3, Takao Sawachi4

1Department of Architecture, School of Engineering, The University of Tokyo, Japan; 2Taisei Corporation, Tokyo, Japan; 3Tokyo Denki University, Tokyo, Japan; 4Building Research Institute, Ibaraki, Japan

Aim and Approach

(max 200 words)

Energy conservation in heating, ventilation, and air conditioning (HVAC) systems has been a major issue due to their high energy consumption percentage of the total energy used in buildings [1]. Therefore, various energy-saving controls related to automated controls such as variable air volume (VAV) control, variable water volume (VWV) control, and chiller unit control have been adopted. However, it is not easy to investigate appropriate control logics and parameters in the design phase and quantify their energy-saving effect [2]. In the operation phase, rooms are often utilized differently from conditions assumed in the design phase; however, reliable energy conservation is strongly demanded under such circumstances. Therefore, an advanced simulation tool that can properly evaluate energy-saving controls is needed even in the design phase [3,4].

In this study, we built a detailed and comprehensive simulation program for an HVAC system including automated controls, and investigated appropriate control logics and parameters for a VAV system depending on unevenly distributed internal loads and damper characteristics through case studies. Because the quantitative energy-saving effect obtained by this simulation program has been difficult to calculate by conventional ones, it is expected to provide useful information for the energy-saving building certification system that provides incentives.

Scientific Innovation and Relevance

(max 200 words)

This study contributes to the new body of knowledge by presenting some of the probable operational issues of VAV control systems that have been neglected in the design phase and at the start of the operation, such as unevenly distributed internal loads among zones, using a simulation. Another significant achievement is clarifying the effects of control logics and parameters on the performance of energy-saving controls related to automatic controls based on simulation results.

To calculate these impacts at the building level, we developed a dynamic simulation in Python with the following three features.

i) Calculate the power of pumps and fans theoretically based on physical models considering pressure distribution

ii) Incorporate the control models, for example, proportional-integral (PI) control and VAV control such as static pressure set-point reset, and easy to change these control logics and parameters

iii) Calculate whole building state from building envelope to VAV dampers

Despite the current high energy performance of VAV control systems, their energy-saving potential can still be improved [2]. This study has great potential for VAV control systems with higher energy efficiency when combined with relevant studies such as automated fault detection and diagnosis.

Preliminary Results and Conclusions

(max 200 words)

For an existing experimental RC building with six rooms, we built a simulation in which almost the same control logic as the real one was incorporated, with a 5-second interval. As a base case, the calculation with the input of a summer representative day resulted in accord with the control logic for each part, such as chiller, airflow, air handling unit, VAV system, and indoor environment. From these results, the validity of the simulation was demonstrated.

From the case studies conducted using the simulation, we obtained the following results.

Case 1) Unevenly distributed internal loads; when loads in some rooms were larger than in the other rooms and the flow rate set-point stuck to the maximum, it adversely affected the controllability of the other rooms with lower loads. A suitable set of control logics and parameters was further investigated.

Case 2) Control logic adjusted to suit equipment characteristics; when the range of the suitable static pressure was adjusted corresponding to the damper characteristics, the control of supply air fans was more stabilized.

These findings suggest an appropriate set of control logics and parameters for expected operating conditions can improve both energy efficiency and indoor air quality in VAV control systems.

Main References

(max 200 words)

[1] Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and buildings, 40(3), 394-398.

[2] Okochi, G. S., & Yao, Y. (2016). A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems. Renewable and Sustainable Energy Reviews, 59, 784-817.

[3] Motomura, A., Akashi, Y., Lim, J., Zhang, W., Miyata, S., Sawachi, T., & Akamine, Y. (2019). Evaluation method for energy saving effects by VAV/VWV control in buildings Part 3-4 [in Japanese]. Proceeding from The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan conference 2019. Sapporo (Japan), 18-20 September 2019.

[4] Yamamoto, S., Akashi, Y., Miyata, S., Zhang, W., Akamine, Y., & Sawachi, T. (2020). Evaluation method for energy saving effects by VAV/VWV control in buildings Part 6 [in Japanese]. Proceeding from The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan conference 2020. Online, 16-18 September 2020.



11:06 - 11:24

Development of test procedure for the evaluation of building energy simulation tools - phase III addition of subsystem test toward systematic diagnostics for HVAC system simulation-

Sei Ito1, Eikichi Ono2, Harunori Yoshida3, Hiromasa Yamaguchi4, Eisuke Togashi5, Kazuki Yajima6, Hiroshi Ninomiya7, Koichi Shinagawa8

1Shimizu Corporation, Japan; 2National University of Singapore, Singapore; 3Professor Emeritus of Kyoto University, Japan; 4Kansai Electric Power Co., Inc., Japan; 5Kogakuin University, Japan; 6SHINRYO CORPORATION, Japan; 7Nikken Sekkei Ltd., Japan; 8Nihon Sekkei, Inc., Japan

Aim and Approach

(max 200 words)

Energy performance simulation of HVAC systems is a key element for system performance evaluation in a design phase, energy conservation code compliance, a green building certification program such as LEED, commissioning of the systems in a construction and operation phase, etc. Under such circumstances, the need for reliability evaluation of simulation tools is increasing. ASHRAE Standard 140 is a well-known test procedure for said purpose, but building fabrics and HVAC systems commonly used in Japan is out of scope. Therefore, aiming for the development of a test procedure focusing on those issues, a technical committee of the Society of Heating, Air-Conditioning and Sanitary Engineers of Japan (SHASE) was established and published a guideline of the procedure in 2016, the outline of which was presented at Building Simulation 2017. At Building Simulation 2019 we also presented the simulation trial results of the expanded test cases by several users, where we showed that test results indicated some discrepancies in energy consumption between tools and test participants. In this report, we report the further investigation about the developed method to identify the causes of the discrepancies with the results of additional test trials.

Scientific Innovation and Relevance

(max 200 words)

The test results of HVAC system simulation reflect the tool’s model characteristics and input errors or lack of modeling knowledge of users, which may cause the discrepancy in the results between tools. Because of such complexity, it is difficult to identify the cause of the discrepancy by using the annual simulation results of the whole HVAC system.

The air-side HVAC equipment analytical verification tests on ASHRAE Standard 140 uses an approach to test simulation results of several HVAC subsystem types mainly based on evaluation of thermal load by changing the input conditions step-by-step under steady state conditions. Although our approach is basically the same as we expand the approach by adding the evaluation of the state and energy performance of the system to focus on characteristics of equipment, control strategy and the system. We divide the whole system into three subsystems which are cooling water, heat source, and air handling unit subsystem. A set of test cases with different input conditions for each subsystem test can allow us to detect and diagnose faults in the tested tool and mistakes in input by users by evaluating whether the expected qualitative difference between the cases can be obtained on a step-by-step basis.

Preliminary Results and Conclusions

(max 200 words)

A simulation test trial was conducted for the three subsystems. For example, regarding the cooling water subsystem test, there are six test cases that differ in the input conditions from the base case, which are outside wet bulb air temperature, load factor, control set points of outlet temperature and temperature difference of cooling water. By comparing the results of the cases, we could confirm whether the cooling tower performance and the control of the variable flow rate of the cooling water pump, etc. were properly modeled and simulated. In addition, we found that this subsystem test can suggest the probable causes of the difference in results such as modeling faults in tools and user mistakes and misunderstanding in input parameters of equipment and control logic characteristics by comparing the step-by-step results of a tool qualitatively. In conclusion, the subsystem test has been shown to have the following three advantages; 1) to be able to compare the characteristics of the tools and clarify their characteristics, 2) to be able to provide the tool developer with a test method that is useful for finding program faults, and 3) to help tool users discover input errors and misunderstandings with regard to modeling.

Main References

(max 200 words)

ASHRAE (2017). ASHRAE Standard 140-2017, Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Atlanta, GA: ASHRAE.

R. Judkoff and J. Neymark (1995). International Energy Agency Building Energy Simulatin Test (BESTEST) and Diagnostic Method, National Renewable Energy Laboratory.

SHASE (2016). SHASE-G 1008-2016, Guideline of Test Procedure for the Evaluation of Building Energy Simulation Tool. Society of Heating, Air-Conditioning and Sanitary Engineers of Japan.

J. Neymark, M. Kennedy, R. Judkoff (2017). Airside HVAC Air-Distribution System Model Test Cases for ASHRAE Standard 140. Proceedings of the 15th International Building Performance Simulation Association Conference, Building Simulation 2017, San Francisco: pp.644-653.

E. Ono, S. Ito, H. Yoshida (2017). Development of Test Procedure for the Evaluation of Building Energy Simulation Tools. Proceedings of the 15th International Building Performance Simulation Association Conference, Building Simulation 2017, San Francisco: pp.380-388.

E. Ono, S. Ito, H. Yoshida (2019). Development of Test Procedure for the Evaluation of Building Energy Simulation Tools –Phase II Expansion of Evaluation Targets and Results of Simulation Trials-. Proceedings of the 16th International Building Performance Simulation Association Conference, Building Simulation 2019, Rome: pp.4578-4585.



11:24 - 11:42

Transfer learning based inverse modeling to identify unknown building properties

Yun-Dam Ko, Cheol-Soo Park

Seoul National University, Korea, Republic of (South Korea)

Aim and Approach

(max 200 words)

Due to lack of detailed building energy data (e.g. thermal properties of building envelopes, infiltration, occupant behavior, hourly sub-metered energy data etc.), most building energy benchmarking systems rely on EUI (Energy Use Intensity, kwh/m2.yr) for energy benchmarking. Precisely, it is closer to ‘energy use benchmarking’ than ‘performance benchmarking’. In addition, the current benchmarking system is conducted for buildings of a same use type, e.g. office, education, hospital, etc.

In this regard, this study aims to present a data-driven methodology to identify building characteristics (e.g. thermal properties of opaque and transparent envelopes, efficiencies of mechanical systems, operational information [setpoint temperature, operation hours]) from energy use data (e.g. monthly gas and electricity use), which will lead to performance benchmarking.

For this purpose, Domain Adaptive Transfer Learning (DATL) is selected. A DATL model will be trained with virtual data and be used for benchmarking for 100,000 existing buildings located in Seoul, South Korea. This proposed approach will provide identification of the aforementioned unmeasured/uncertain building characteristics and will be used to categorize buildings into peer groups by ‘similar’ performance characteristics.

Scientific Innovation and Relevance

(max 200 words)

To overcome the lack of the detailed building energy data mentioned in “Aim and Approach”, the authors propose the DATL based approach. It is expected that this approach enables to identify unmeasured important features based on measured energy use, and it could be a good alternative when detailed building data are not available.

As a result, the proposed approach can provide more rational building energy ‘performance’ benchmarking. While the conventional approach (i.e. energy use benchmarking) cannot identify the reason for high energy consumption, the proposed approach, performance benchmarking enhanced by the transfer learning algorithm, is expected to inform decision makers of the degree of energy performance of a given building, e.g. bad envelopes, low efficiencies of mechanical systems, etc.

Preliminary Results and Conclusions

(max 200 words)

Firstly, EnergyPlus reference buildings will be sampled using Latin Hypercube Sampling for ‘source domain’ data. A DATL model will be trained from the generated source domain data. This model will be used to identify thermal properties of building envelopes and efficiencies of HVAC systems, given monthly gas and electricity use data. The developed model will be tested with available data gathered from 100,000 existing buildings located in Seoul, South Korea.

Then, the authors will categorize buildings into peer groups in terms of similar ‘energy performance’ using a clustering algorithm, e.g. K-means. Case studies will be conducted to compare benchmarking results of our proposed approach with two conventional approaches (by use type and by daily load profile).

Main References

(max 200 words)

Luo, X., Hong, T., Chen, Y. and Piette, M.A. (2017), Electric load shape benchmarking for small- and medium-sized commercial buildings, Applied Energy, 204, 715-725.

Gao, X. and Malkawi, A. (2014), A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm, Energy and Buildings, 84, 607-616.

Park, J.Y., Yang, X., Miller, C., Arjunan, P. and Nagy, Z. (2019), Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset, Applied Energy, 236, 1280-1295.

Westermann, P., Deb, C., Schlueter, A. and Evins, R. (2020), Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data, Applied Energy, 264, 114715

Zhan, S., Liu, Z., Chong, A. and Yan, D. (2020), Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking, Applied Energy, 269, 114920



11:42 - 12:00

Meta-modelling of operation schedules of commercial buildings based on measured electricity demand data

Yohei Yamaguchi, Fumiya Enokihara, Yoshiyuki Shimoda

Osaka University, Japan

Aim and Approach

(max 200 words)

In simulations of the energy demand of commercial buildings, the assumptions regarding the building operations characterise the magnitude and temporal variation of the energy demand. However, the operating conditions of building stock are rarely available; thus, building stock and urban building energy models are often unable to simulate realistic time variations in energy demand. To address this issue, this paper presents a method for extracting an operation schedule from the time-series data of an electricity demand. A building’s electricity demand is considered as the sum of the electricity consumption of all ofall the appliances used in the building. The appliances are divided into four groups based on two factors: 1) the existence of operations by building occupants, and 2) the existence of seasonal changes in electricity consumption. In the proposed method, a load disaggregation method is applied to extract the load component driven by the operations of building occupants without seasonal changes as a week-length profile. The profile is further summarised using a few characterisation parameters. Finally, joint probability distributions of the characterisation parameters are developed to assign operation conditions to the simulated buildings. This method is applied to a few thousand commercial buildings in Japan.

Scientific Innovation and Relevance

(max 200 words)

The methods for considering realistic operation conditions in building stock energy models can be divided into the following three groups: (1) those using stochastic activity-based models to stochastically simulate occupancy conditions based on survey-based data, (2) those using building occupancy conditions extracted based on personal location data, and (3) those using operation schedules derived based on time-series electricity demand data measured in buildings (Bianchi et al., 2020). This study establishes a method for the third group. The time-series electricity demand data is first disaggregated by using a load profile disaggregation method to extract the average profile of a one-week period, i.e. 24 hours × 7 days = a 168-hour profile; in this profile, the seasonal variation is removed. The average week-length profile represents the magnitude of the building operation at each time of the day of the week. Then, the week-length profile is used to extract parameters representing the relationships between the times of day, days of the week, and magnitudes of the profile. Finally, parameters derived from sample buildings are used to develop statistical distributions of the parameters as a representation of the operation conditions of buildings within a building group.

Preliminary Results and Conclusions

(max 200 words)

This paper first describes the detailed processes of the demand disaggregation, parameterisation of the operation conditions, and development of joint probability distributions of the parameters, i.e. the start time and duration of the operation. The results show that the extracted operation schedules exhibit general characteristics for each business sector. Most office buildings have a sharp rise in the morning, a slow decrease during evening hours, and low values during night hours and weekend days. The distributions of the characteristics of the school buildings are similar to those of offices. A hotel's start times are concentrated in the early morning, whereas the operation duration is much longer than those of the office and school. A hospital also has an early start time, but the operation duration decreases with a delay in the start time. A similar trend is observed for retail. A restaurant shows a diverse pattern for the start time and duration of operation, characterising the differences in the opening hours. The results demonstrate that the method is useful for capturing the variations in the operation conditions of buildings. The statistical distributions can be used to assign building operation conditions to the reference building models used in building stock models.

Main References

(max 200 words)

Bianchi, C., Zhang, L, Goldwasser, D, Parker, A, Horsey, H. (2020). Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules. Applied Energy; 276:115470. doi:10.1016/j.apenergy.2020.115470.



 
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