IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v4y2019i2p72-d232904.html
   My bibliography  Save this article

Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities

Author

Listed:
  • Abhishek Gaur

    (Construction Research Center, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada)

  • Michael Lacasse

    (Construction Research Center, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada)

  • Marianne Armstrong

    (Construction Research Center, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada)

Abstract

Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected future climates. Hygrothermal and whole building simulation models, which are important tools for assessing performance, require continuous climate records at high temporal frequencies of a wide range of climate variables for input into the kinds of models that relate to solar radiation, cloud-cover, wind, humidity, rainfall, temperature, and snow-cover. In this study, climate data that can be used to assess the performance of building envelopes under current and projected future climates, concurrent with 2 °C and 3.5 °C increases in global temperatures, are generated for 11 major Canadian cities. The datasets capture the internal variability of the climate as they are comprised of 15 realizations of the future climate generated by dynamically downscaling future projections from the CanESM2 global climate model and thereafter bias-corrected with reference to observations. An assessment of the bias-corrected projections suggests, as a consequence of global warming, future increases in the temperatures and precipitation, and decreases in the snow-cover and wind-speed for all cities.

Suggested Citation

  • Abhishek Gaur & Michael Lacasse & Marianne Armstrong, 2019. "Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities," Data, MDPI, vol. 4(2), pages 1-17, May.
  • Handle: RePEc:gam:jdataj:v:4:y:2019:i:2:p:72-:d:232904
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/4/2/72/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/4/2/72/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Delia D’Agostino & Paolo Zangheri & Luca Castellazzi, 2017. "Towards Nearly Zero Energy Buildings in Europe: A Focus on Retrofit in Non-Residential Buildings," Energies, MDPI, vol. 10(1), pages 1-15, January.
    2. Jin-Hee Kim & Ha-Ryeon Kim & Jun-Tae Kim, 2015. "Analysis of Photovoltaic Applications in Zero Energy Building Cases of IEA SHC/EBC Task 40/Annex 52," Sustainability, MDPI, vol. 7(7), pages 1-19, July.
    3. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    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. Casey R. Corrado & Suzanne M. DeLong & Emily G. Holt & Edward Y. Hua & Andreas Tolk, 2022. "Combining Green Metrics and Digital Twins for Sustainability Planning and Governance of Smart Buildings and Cities," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    2. Juan Botero-Valencia & Luis Castano-Londono & David Marquez-Viloria, 2022. "Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments," Data, MDPI, vol. 7(6), pages 1-15, June.
    3. Abhishek Gaur & Michael Lacasse, 2022. "Climate Data to Support the Adaptation of Buildings to Climate Change in Canada," Data, MDPI, vol. 7(4), pages 1-22, April.

    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. Kubik, M.L. & Coker, P.J. & Hunt, C., 2012. "The role of conventional generation in managing variability," Energy Policy, Elsevier, vol. 50(C), pages 253-261.
    2. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    3. Ernestyna Szpakowska-Loranc, 2021. "Multi-Attribute Analysis of Contemporary Cultural Buildings in the Historic Urban Fabric as Sustainable Spaces—Krakow Case Study," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    4. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    5. López-Ochoa, Luis M. & Las-Heras-Casas, Jesús & López-González, Luis M. & Olasolo-Alonso, Pablo, 2019. "Towards nearly zero-energy buildings in Mediterranean countries: Energy Performance of Buildings Directive evolution and the energy rehabilitation challenge in the Spanish residential sector," Energy, Elsevier, vol. 176(C), pages 335-352.
    6. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    7. Kittisak Lohwanitchai & Daranee Jareemit, 2021. "Modeling Energy Efficiency Performance and Cost-Benefit Analysis Achieving Net-Zero Energy Building Design: Case Studies of Three Representative Offices in Thailand," Sustainability, MDPI, vol. 13(9), pages 1-24, May.
    8. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    9. Kim, Min-Hwi & Kim, Deukwon & Heo, Jaehyeok & Lee, Dong-Won, 2020. "Energy performance investigation of net plus energy town: Energy balance of the Jincheon Eco-Friendly energy town," Renewable Energy, Elsevier, vol. 147(P1), pages 1784-1800.
    10. Liu, Wen & Hu, Weihao & Lund, Henrik & Chen, Zhe, 2013. "Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China," Applied Energy, Elsevier, vol. 104(C), pages 445-456.
    11. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    12. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    13. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    14. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    15. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    16. Seyed Reza Mirnezami & Amin Mohseni Cheraghlou, 2022. "Wind Power in Iran: Technical, Policy, and Financial Aspects for Better Energy Resource Management," Energies, MDPI, vol. 15(9), pages 1-18, April.
    17. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    18. Anti Hamburg & Targo Kalamees, 2018. "The Influence of Energy Renovation on the Change of Indoor Temperature and Energy Use," Energies, MDPI, vol. 11(11), pages 1-15, November.
    19. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    20. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.

    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:jdataj:v:4:y:2019:i:2:p:72-:d:232904. 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.