IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v478y2023ics0304380023000303.html
   My bibliography  Save this article

A dynamic thermal model for predicting internal temperature of tree cavities and nest boxes

Author

Listed:
  • Velander, Taylor B.
  • Joyce, Michael J.
  • Kujawa, Angela M.
  • Sanders, Robert L.
  • Keenlance, Paul W.
  • Moen, Ron A.

Abstract

•We describe a theoretical model for estimating tree cavity temperature over time.•The model uses first principles of heat and mass transfer equations.•Predicted temperature values were accurate for natural and artificial cavities.•The model is a useful tool for understanding animal behavior and physiology.

Suggested Citation

  • Velander, Taylor B. & Joyce, Michael J. & Kujawa, Angela M. & Sanders, Robert L. & Keenlance, Paul W. & Moen, Ron A., 2023. "A dynamic thermal model for predicting internal temperature of tree cavities and nest boxes," Ecological Modelling, Elsevier, vol. 478(C).
  • Handle: RePEc:eee:ecomod:v:478:y:2023:i:c:s0304380023000303
    DOI: 10.1016/j.ecolmodel.2023.110302
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023000303
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110302?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    2. Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
    Full references (including those not matched with items on IDEAS)

    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. Jefferson Brooks & Ana Rivera & Miguel Chen Austin & Nathalia Tejedor-Flores, 2022. "A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    2. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    3. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    4. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    5. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    6. Song, Jiancai & Bian, Tianxiang & Xue, Guixiang & Wang, Hanyu & Shen, Xingliang & Wu, Xiangdong, 2023. "Short-term forecasting model for residential indoor temperature in DHS based on sequence generative adversarial network," Applied Energy, Elsevier, vol. 348(C).
    7. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    8. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    9. Meng, Xiangxin & Liu, Yan & Wang, Shangyu & Chen, Feiyu & Cao, Qimeng & Yang, Liu, 2022. "A fast solar architecture design method towards zero heating energy: A SHF-SLR-based model and its parameters," Energy, Elsevier, vol. 258(C).
    10. Alejandra Aversa & Luis Ballestero & Miguel Chen Austin, 2022. "Highlighting the Probabilistic Behavior of Occupants’ Preferences in Energy Consumption by Integrating a Thermal Comfort Controller in a Tropical Climate," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
    11. Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    12. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    13. Huang, Sen & Lin, Yashen & Chinde, Venkatesh & Ma, Xu & Lian, Jianming, 2021. "Simulation-based performance evaluation of model predictive control for building energy systems," Applied Energy, Elsevier, vol. 281(C).
    14. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    15. Ali Hamza & Muhammad Uneeb & Iftikhar Ahmad & Komal Saleem & Zunaib Ali, 2023. "Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones," Energies, MDPI, vol. 16(10), pages 1-27, May.
    16. Wang, Xiaoyu & Tian, Shuai & Ren, Jiawen & Jin, Xing & Zhou, Xin & Shi, Xing, 2024. "A novel resistance-capacitance model for evaluating urban building energy loads considering construction boundary heterogeneity," Applied Energy, Elsevier, vol. 361(C).
    17. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
    18. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    19. Pal, Monalisa & Alyafi, Amr Alzouhri & Ploix, Stéphane & Reignier, Patrick & Bandyopadhyay, Sanghamitra, 2019. "Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook," Applied Energy, Elsevier, vol. 238(C), pages 1452-1470.
    20. Alastaire Sena Alinsato & Kora Hafiz Bete & Nassibou Bassongui, 2023. "A climate–economy model in a stochastic differential equilibrium with fractional Brownian motions and Poisson jumps," SN Business & Economics, Springer, vol. 3(8), pages 1-23, August.

    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:eee:ecomod:v:478:y:2023:i:c:s0304380023000303. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.