IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4935-d416417.html
   My bibliography  Save this article

Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data

Author

Listed:
  • Sang-Woo Ha

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Seung-Hoon Park

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

  • Jae-Yong Eom

    (R&D Division, EAGON Windows&Doors Co., Ltd., Incheon 22107, Korea)

  • Min-Suk Oh

    (R&D Division, DAEJIN, Seoul 05839, Korea)

  • Ga-Young Cho

    (Department of Smart City Research, Seoul Institute of Technology, Seoul 03909, Korea)

  • Eui-Jong Kim

    (Department of Architectural Engineering, Inha University, Incheon 22212, Korea)

Abstract

Installing renewable energy systems for zero-energy buildings has become increasingly common; building integrated photovoltaic (BIPV) systems, which integrate PV modules into the building envelope, are being widely selected as renewable systems. In particular, owing to the rapid growth of information and communication technology, the requirement for appropriate operation and control of energy systems has become an important issue. To meet these requirements, a computational model is essential; however, some unmeasurable parameters can result in inaccurate results. This work proposes a calibration method for unknown parameters of a well-known BIPV model based on in situ test data measured over eight days; this parameter calibration was conducted via an optimization algorithm. The unknown parameters were set such that the results obtained from the BIPV simulation model are similar to the in situ measurement data. Results of the calibrated model indicated a root mean square error (RMSE) of 3.39 °C and 0.26 kW in the BIPV cell temperature and total power production, respectively, whereas the noncalibrated model, which used typical default values for unknown parameters, showed an RMSE of 6.92 °C and 0.44 kW for the same outputs. This calibration performance was quantified using measuring data from the first four days; the error increased slightly when data from the remaining four days were compared for the model tests.

Suggested Citation

  • Sang-Woo Ha & Seung-Hoon Park & Jae-Yong Eom & Min-Suk Oh & Ga-Young Cho & Eui-Jong Kim, 2020. "Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data," Energies, MDPI, vol. 13(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4935-:d:416417
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4935/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4935/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yadav, Somil & Panda, S.K., 2020. "Thermal performance of BIPV system by considering periodic nature of insolation and optimum tilt-angle of PV panel," Renewable Energy, Elsevier, vol. 150(C), pages 136-146.
    2. Debbarma, Mary & Sudhakar, K. & Baredar, Prashant, 2017. "Thermal modeling, exergy analysis, performance of BIPV and BIPVT: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1276-1288.
    3. Hyung Jun An & Jong Ho Yoon & Young Sub An & Eunnyeong Heo, 2018. "Heating and Cooling Performance of Office Buildings with a-Si BIPV Windows Considering Operating Conditions in Temperate Climates: The Case of Korea," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    4. Alvarez-Herranz, Agustin & Balsalobre-Lorente, Daniel & Shahbaz, Muhammad & Cantos, José María, 2017. "Energy innovation and renewable energy consumption in the correction of air pollution levels," Energy Policy, Elsevier, vol. 105(C), pages 386-397.
    5. Chul-sung Lee & Hyo-mun Lee & Min-joo Choi & Jong-ho Yoon, 2019. "Performance Evaluation and Prediction of BIPV Systems under Partial Shading Conditions Using Normalized Efficiency," Energies, MDPI, vol. 12(19), pages 1-16, October.
    6. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    7. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jong Rok Lim & Woo Gyun Shin & Chung Geun Lee & Yong Gyu Lee & Young Chul Ju & Suk Whan Ko & Jung Dong Kim & Gi Hwan Kang & Hyemi Hwang, 2020. "A Study of the Electrical Output and Reliability Characteristics of the Crystalline Photovoltaic Module According to the Front Materials," Energies, MDPI, vol. 14(1), pages 1-10, December.
    2. Gambade, Julien & Noël, Hervé & Glouannec, Patrick & Magueresse, Anthony, 2023. "Numerical model of intermittent solar hot water production," Renewable Energy, Elsevier, vol. 218(C).
    3. Wijeratne, W.M. Pabasara Upalakshi & Samarasinghalage, Tharushi Imalka & Yang, Rebecca Jing & Wakefield, Ron, 2022. "Multi-objective optimisation for building integrated photovoltaics (BIPV) roof projects in early design phase," Applied Energy, Elsevier, vol. 309(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Skandalos, Nikolaos & Wang, Meng & Kapsalis, Vasileios & D'Agostino, Delia & Parker, Danny & Bhuvad, Sushant Suresh & Udayraj, & Peng, Jinqing & Karamanis, Dimitris, 2022. "Building PV integration according to regional climate conditions: BIPV regional adaptability extending Köppen-Geiger climate classification against urban and climate-related temperature increases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    2. Wei, Jian & Zhou, Yuqi & Wang, Yuan & Miao, Zhuang & Guo, Yupeng & Zhang, Hao & Li, Xueting & Wang, Zhipeng & Shi, Zongmo, 2023. "A large-sized thermoelectric module composed of cement-based composite blocks for pavement energy harvesting and surface temperature reducing," Energy, Elsevier, vol. 265(C).
    3. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    4. Zhongwei, Huang & Liu, Yishu, 2022. "The role of eco-innovations, trade openness, and human capital in sustainable renewable energy consumption: Evidence using CS-ARDL approach," Renewable Energy, Elsevier, vol. 201(P1), pages 131-140.
    5. Hille, Erik & Althammer, Wilhelm & Diederich, Henning, 2020. "Environmental regulation and innovation in renewable energy technologies: Does the policy instrument matter?," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    6. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
    7. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    8. Kazimierz Kawa & Rafał Mularczyk & Waldemar Bauer & Katarzyna Grobler-Dębska & Edyta Kucharska, 2024. "Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings," Energies, MDPI, vol. 17(13), pages 1-16, June.
    9. Kazemzadeh, Emad & Fuinhas, José Alberto & Koengkan, Matheus & Shadmehri, Mohammad Taher Ahmadi, 2023. "Relationship between the share of renewable electricity consumption, economic complexity, financial development, and oil prices: A two-step club convergence and PVAR model approach," International Economics, Elsevier, vol. 173(C), pages 260-275.
    10. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    11. Udi Joshua & Festus V. Bekun & Samuel A. Sarkodie, 2020. "New Insight into the Causal Linkage between Economic Expansion, FDI, Coal consumption, Pollutant emissions and Urbanization in South Africa," Working Papers 20/011, European Xtramile Centre of African Studies (EXCAS).
    12. Liang, Shen & Zheng, Hongfei & Wang, Xuanlin & Ma, Xinglong & Zhao, Zhiyong, 2022. "Design and performance validation on a solar louver with concentrating-photovoltaic-thermal modules," Renewable Energy, Elsevier, vol. 191(C), pages 71-83.
    13. Caglar, Abdullah Emre & Daştan, Muhammet & Avci, Salih Bortecine, 2024. "Persistence of disaggregate energy RD&D expenditures in top-five economies: Evidence from artificial neural network approach," Applied Energy, Elsevier, vol. 365(C).
    14. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    15. Malayaranjan Sahoo & Narayan Sethi, 2022. "The dynamic impact of urbanization, structural transformation, and technological innovation on ecological footprint and PM2.5: evidence from newly industrialized countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 4244-4277, March.
    16. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    17. Andrzej Pacana & Karolina Czerwińska & Grzegorz Ostasz, 2023. "Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity," Energies, MDPI, vol. 16(8), pages 1-26, April.
    18. Mary O. Agboola & Festus V. Bekun, 2019. "Does Agricultural Value Added Induce Environmental Degradation? Empirical Evidence from an Agrarian Country," CEREDEC Working Papers 19/040, Centre de Recherche pour le Développement Economique (CEREDEC).
    19. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    20. Gigih Rahmandhani Setyantho & Hansaem Park & Seongju Chang, 2021. "Multi-Criteria Performance Assessment for Semi-Transparent Photovoltaic Windows in Different Climate Contexts," Sustainability, MDPI, vol. 13(4), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4935-:d:416417. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.