IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i9p864-d76932.html
   My bibliography  Save this article

Reducing Simulation Performance Gap in Hemp-Lime Buildings Using Fourier Filtering †

Author

Listed:
  • Ljubomir Jankovic

    (Zero Carbon Lab, Birmingham City University, Birmingham B5 5JU, UK)

Abstract

Mainstream dynamic simulation tools used by designers do not have a built-in capability to accurately simulate the effect of hemp-lime on building temperature and relative humidity. Due to the specific structure of hemp-lime, heat travels via a maze of solid branches whilst the capillary tubes absorb and release moisture. The resultant heat and moisture transfer cannot be fully represented in mainstream simulation tools, causing a significant performance gap between the simulation and the actual performance. The author has developed an analysis method, based on a numerical procedure for digital signal filtering using Fourier series. The paper develops and experimentally validates transfer functions that enhance simulation results and enable accurate representation of behaviour of buildings built from hemp-lime material using the results of a post-occupancy research project. As a performance gap between design simulation and actual buildings occurs in relation to all buildings, this method has a wider scope of application in reducing the performance gap.

Suggested Citation

  • Ljubomir Jankovic, 2016. "Reducing Simulation Performance Gap in Hemp-Lime Buildings Using Fourier Filtering †," Sustainability, MDPI, vol. 8(9), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:9:p:864-:d:76932
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/9/864/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/9/864/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    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. Hao Wang & Pen-Chi Chiang & Yanpeng Cai & Chunhui Li & Xuan Wang & Tse-Lun Chen & Shiming Wei & Qian Huang, 2018. "Application of Wall and Insulation Materials on Green Building: A Review," Sustainability, MDPI, vol. 10(9), pages 1-21, September.

    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. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    2. Mehleri, E.D. & Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C., 2010. "A new neural network model for evaluating the performance of various hourly slope irradiation models: Implementation for the region of Athens," Renewable Energy, Elsevier, vol. 35(7), pages 1357-1362.
    3. Jorge E. De León-Ruiz & Ignacio Carvajal-Mariscal & Antonin Ponsich, 2019. "Feasibility Analysis and Performance Evaluation and Optimization of a DXSAHP Water Heater Based on the Thermal Capacity of the System: A Case Study," Energies, MDPI, vol. 12(20), pages 1-38, October.
    4. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    5. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    6. Abu Bakar, Nur Najihah & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah & Bandi, Masilah, 2015. "Energy efficiency index as an indicator for measuring building energy performance: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 1-11.
    7. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    8. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
    9. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    10. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
    11. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    12. Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
    13. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    14. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
    15. Abdulazeez Rotimi & Ali Bahadori-Jahromi & Anastasia Mylona & Paulina Godfrey & Darren Cook, 2017. "Estimation and Validation of Energy Consumption in UK Existing Hotel Building Using Dynamic Simulation Software," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
    16. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    17. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
    18. Wang, Jiangfeng & Sun, Zhixin & Dai, Yiping & Ma, Shaolin, 2010. "Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network," Applied Energy, Elsevier, vol. 87(4), pages 1317-1324, April.
    19. Bai, Yefei & Yu, Cong & Pan, Wei, 2024. "Systematic examination of energy performance gap in low-energy buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    20. Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:8:y:2016:i:9:p:864-:d:76932. 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.