Building Simulation

, Volume 11, Issue 3, pp 497–506 | Cite as

A dynamic modelling approach for simulating climate change impact on energy and hygrothermal performances of wood buildings

Research Article Building Thermal, Lighting, and Acoustics Modeling
First Online:
Received:
Revised:
Accepted:
  • 50 Downloads

Abstract

paper presents an improved physics based dynamic modelling approach to simulating wooden buildings’ hygrothermal performances under climate change. As the only mainstream renewable building material, wood is widely used in buildings especially in Europe and Northern America. The proposed model is intended to be efficient and adaptable to address one of the major challenges in climate change research: some uncertainties prevail now but will be resolved gradually as time passes. The applicability and practicality of the model are illustrated and tested by a wooden church equipped with well designed and high precision measurements. Model predictions and forecasts are in good agreement to the measurements despite the description of the dynamic model is simple. The model is further applied to a future projection of climate indoors to examine future climate impacts on buildings and to assess the climatic suitability of wood for providing a mechanism that can facilitate the reduction of climate risks and be more resilient to global warming. The paper suggests that wood building materials offer an effective and resilient response to anticipated future climate changes. While predominantly focused on wooden buildings, the model is general enough for other types of buildings.

Keywords

modelling method physics based dynamic model forecast climate change impact wooden buildings energy and hygrothermal performance 
This is a preview of subscription content, log in to check access

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This study is part of the WOODLIFE project, which is financed by the Aalto University’s Energy-efficiency research programme. The Finnish Cultural Foundation, Association for Promoting Technology, the Finnish Science Foundation for Economics and Technology, Modern Wooden Town Graduate School and the Doctoral Programme of the Built Environment have also funded this work. We would like to thank Professor Matti Kairi, Jonna Silvo, Tuula Noponen, Simo Koponen, and Olli Paajanen for providing the data and supporting documents.

References

  1. Ambrose D, Gustavsson L, Sathre R (2016). Climate impacts of wood vs. non-wood buildings. Sveriges Kommuner och Landsting, Sweden.Google Scholar
  2. Asimakopoulos DA, Santamouris M, Farrou I, Laskari M, Saliari M, et al. (2012) Modelling the energy demand projection of the building sector in Greece in the 21st century. Energy and Buildings, 49: 488–498.CrossRefGoogle Scholar
  3. Bertolin C, Camuffo D, Bighignoli I (2015). Past reconstruction and future forecast of domains of indoor relative humidity fluctuations calculated according to EN 15757:2010. Energy and Buildings, 102: 197–206.CrossRefGoogle Scholar
  4. Box GEP, Jenkins GM, Reinsel GC (1994). Time Series Analysis: Forecasting and Control, 3rd edn. Englewood Cliffs, NJ, USA: Prentice Hall.MATHGoogle Scholar
  5. CEI-Bois (2011). Tackle Climate Change: Use Wood. Building Information Foundation RTS. European Confederation of Woodworking Industries.Google Scholar
  6. Chan ALS (2011). Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong. Energy and Buildings, 43: 2860–2868.CrossRefGoogle Scholar
  7. Chinowsky PS, Schweikert AE, Hayles C, Strzepek NL, Strzepek K (2012). UNU-WIDER White Paper Buildings & Climate Change: Methodology and Areas for Further Research. Available at http://clicslab.org/docs/buildings_climate_change.pdf.Google Scholar
  8. Coyle G (1977). Management System Dynamics. Chichester, UK: John Wiley & Sons.Google Scholar
  9. Cubi E, Bergerson J (2014). Should building energy simulation tools integrate life cycle assessment? A discussion of the potential benefits and challenges. In: Proceedings of eSim 2014, Ottawa, Canada.Google Scholar
  10. de Wilde P, Coley D (2012). The implications of a changing climate for buildings. Building and Environment, 55: 1–7CrossRefGoogle Scholar
  11. Dirks JA, Gorrissen WJ, Hathaway JH, Skorski DC, Scott MJ, Pulsipher TC, Huang M, Liu Y, Rice JS (2015). Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach. Energy, 79: 20–32.CrossRefGoogle Scholar
  12. EIA (2007). International Energy Outlook, Report DOE/EIA-0484. Energy Information Administration, U.S. Department of Energy, Available at http://www.eia.doe.gov/oiaf/ieo.Google Scholar
  13. Hameury S (2006). The hygrothermal inertia of massive timber constructions. PhD Thesis, Royal Institute of Technology, Sweden.Google Scholar
  14. Huijbregts Z, Kramer RP, Martens MHJ, van Schijndel AWM, Schellen HL (2012). A proposed method to assess the damage risk of future climate change to museum objects in historic buildings. Building and Environment, 55: 43–56CrossRefGoogle Scholar
  15. IPCC (2014). Climate Change. Available at https://www.ipcc.ch/ news_and_events/.../ar5_syr_headlines_en.pdf.Google Scholar
  16. Leissner J, Kilian R, Kotova L, et al. (2015). Climate for culture: Assessing the impact of climate change on the future indoor climate in historic buildings using simulations. Heritage Science, 3: 38.CrossRefGoogle Scholar
  17. Lü X, Lu T, Kibert CJ, Viljanen M (2015). Modeling and forecasting energy consumption for heterogeneous buildings using a physicalstatistical approach. Applied Energy, 144: 261–275.CrossRefGoogle Scholar
  18. Masson V, Marchadier C, Adolphe L, Aguejdad R, Avner P, et al. (2014). Adapting cities to climate change: A systemic modelling approach. Urban Climate, 10: 407–429.CrossRefGoogle Scholar
  19. Ministry of Employment and the Economy (2014). The status of wood construction in Finland. Available at https://www.tem.fi/files/ 40816/Wood_Construction_in_Finland.pdf.Google Scholar
  20. Nejat P, Jomehzadeh F, Taheri MM, Gohari M, Majid MZ (2015). A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries. Renewable and Sustainable Energy Reviews, 43: 843–862.CrossRefGoogle Scholar
  21. Pennestri D (2013). The Energy and Environmental Requalification of Post-war Housing: Problematics and Innovative Solutions for the Building Envelope. In: Proceedings of Central Europe towards Sustainable Building, Prague, Czech Republic.Google Scholar
  22. Richardson KA (2002). Methodological implications of complex systems approaches to sociality: Some further remarks. Journal of Artificial Societies and Social Simulation, 5(2): 6. Available at http://jasss.soc.surrey.ac.uk/5/2/6.html.MathSciNetGoogle Scholar
  23. Rode C, Grau K (2008). Moisture buffering and its consequence in whole building hygrothermal modeling. Journal of Building Physics, 31: 333–360.CrossRefGoogle Scholar
  24. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (2007). Climate Change 2007 —The Physical Science Basis. Cambridge, UK: Cambridge University Press.Google Scholar
  25. Suzuki T, Ikeguchi T, Suzuki M (2007). Evaluation of nonlinearity and validity of nonlinear modeling for complex time series. Physical Review E, 76: 046202.CrossRefGoogle Scholar
  26. Wan KKW, Li DHW, Pan W, Lam JC (2012). Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Applied Energy, 97: 274–282.CrossRefGoogle Scholar
  27. Wang H, Chen Q (2014). Impact of climate change heating and cooling energy use in buildings in the United States. Energy and Buildings, 82: 428–436.CrossRefGoogle Scholar
  28. Whetton P, Hennessy K, Clarke J, McInnes K, Kent D (2012). Use of representative climate futures in impact and adaptation assessment. Climatic Change, 115: 433–442.CrossRefGoogle Scholar
  29. White H (2000). A reality check for data snooping. Econometrica, 68: 1097–1126.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Civil Engineering, School of EngineeringAalto UniversityAaltoFinland
  2. 2.Powell Center for Construction & EnvironmentUniversity of FloridaGainesvilleUSA
  3. 3.Department of Forest Products Technology, School of Chemical TechnologyAalto UniversityAaltoFinland
  4. 4.College of Construction EngineeringJilin UniversityChangchunChina
  5. 5.Key Laboratory of Drilling Technology in Complex Conditions of Ministry of Land and Resources of ChinaChangchunChina

Personalised recommendations