IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4348-d1467699.html
   My bibliography  Save this article

Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation

Author

Listed:
  • Matteo Borrotti

    (Department of Economics, Management and Statistics, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milano, Italy)

Abstract

Building Performance Simulation extensively uses statistical learning techniques for quicker insights and improved accessibility. These techniques help understand the relationship between input variables and the desired outputs, and they can predict unknown observations. Prediction becomes more informative with uncertainty quantification, which involves computing prediction intervals. Conformal prediction has emerged over the past 25 years as a flexible and rigorous method for estimating uncertainty. This approach can be applied to any pre-trained model, creating statistically rigorous uncertainty sets or intervals for model predictions. This study uses data from simulated buildings to demonstrate the powerful applications of conformal prediction in Building Performance Simulation (BPS) and, consequently, to the broader energy sector. Results show that conformal prediction can be applied when any assumptions about input and output variables are made, enhancing understanding and facilitating informed decision-making in energy system design and operation.

Suggested Citation

  • Matteo Borrotti, 2024. "Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation," Energies, MDPI, vol. 17(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4348-:d:1467699
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4348/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4348/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. F. Lawless & Marc Fredette, 2005. "Frequentist prediction intervals and predictive distributions," Biometrika, Biometrika Trust, vol. 92(3), pages 529-542, September.
    2. Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
    3. Olive, David J., 2007. "Prediction intervals for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3115-3122, March.
    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. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    2. Lasanthi C. R. Pelawa Watagoda & David J. Olive, 2021. "Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals," Statistical Papers, Springer, vol. 62(5), pages 2407-2431, October.
    3. Pan, Li & Politis, Dimitris, 2014. "Bootstrap prediction intervals for Markov processes," University of California at San Diego, Economics Working Paper Series qt7555757g, Department of Economics, UC San Diego.
    4. Brian D. Williamson & Peter B. Gilbert & Marco Carone & Noah Simon, 2021. "Nonparametric variable importance assessment using machine learning techniques," Biometrics, The International Biometric Society, vol. 77(1), pages 9-22, March.
    5. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.
    6. Tengyuan Liang, 2022. "Universal prediction band via semi‐definite programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1558-1580, September.
    7. Jorge Navarro & Francesco Buono, 2023. "Predicting future failure times by using quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 543-576, July.
    8. Yuanyuan Shen & Katherine P. Liao & Tianxi Cai, 2015. "Sparse kernel machine regression for ordinal outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 63-70, March.
    9. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
    10. Wang, Hsiuying, 2008. "Coverage probability of prediction intervals for discrete random variables," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 17-26, September.
    11. Vidoni, Paolo, 2015. "Calibrated multivariate distributions for improved conditional prediction," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 16-25.
    12. Haojin Zhou & Tapan Nayak, 2015. "On the equivariance criterion in statistical prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 541-555, June.
    13. Paolo Vidoni, 2017. "Improved multivariate prediction regions for Markov process models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 1-18, March.
    14. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
    15. Solari, Aldo & Djordjilović, Vera, 2022. "Multi split conformal prediction," Statistics & Probability Letters, Elsevier, vol. 184(C).
    16. Monika Kubzova & Vit Krivy & Katerina Kreislova, 2020. "Probabilistic Prediction of Corrosion Damage of Steel Structures in the Vicinity of Roads," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
    17. V. J. Roelofs & F. P. A. Coolen & A. D. M. Hart, 2011. "Nonparametric Predictive Inference for Exposure Assessment," Risk Analysis, John Wiley & Sons, vol. 31(2), pages 218-227, February.
    18. De Oliveira, Victor & Kone, Bazoumana, 2015. "Prediction intervals for integrals of Gaussian random fields," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 37-51.
    19. Paolo Vidoni, 2009. "A simple procedure for computing improved prediction intervals for autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 577-590, November.
    20. Omar M. Bdair & Mohammad Z. Raqab, 2022. "Prediction of future censored lifetimes from mixture exponential distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(7), pages 833-857, 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:jeners:v:17:y:2024:i:17:p:4348-:d:1467699. 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.