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

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  • 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
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    References listed on IDEAS

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    1. Olive, David J., 2007. "Prediction intervals for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3115-3122, March.
    2. J. F. Lawless & Marc Fredette, 2005. "Frequentist prediction intervals and predictive distributions," Biometrika, Biometrika Trust, vol. 92(3), pages 529-542, September.
    3. 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.
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