[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04071903.html
   My bibliography  Save this paper

Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing

Author

Listed:
  • Merlin Keller

    (EDF - Electricité de France)

  • Guillaume Damblin

    (Université Paris-Saclay)

  • Alberto Pasanisi

    (Edison)

  • Mathieu Schumann

    (EDF - Electricité de France)

  • Pierre Barbillon

    (MIA Paris-Saclay - Mathématiques et Informatique Appliquées - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Fabrizio Ruggeri

    (IMATI - Istituto di Matematica Applicata e Tecnologie Informatiche - CNR - National Research Council of Italy | Consiglio Nazionale delle Ricerche)

Abstract
In this paper, we present a case study aimed at determining a billing plan that ensures customer loyalty and provides a profit for the energy company, whose point of view is taken in the paper. The energy provider promotes new contracts for residential buildings, in which customers pay a fixed rate chosen in advance, based on an overall energy consumption forecast. For such a purpose, we consider a practical Bayesian framework for the calibration and validation of a computer code used to forecast the energy consumption of a building. On the basis of power field measurements, collected from an experimental building cell in a given period of time, the code is calibrated, effectively reducing the epistemic uncertainty affecting the most relevant parameters of the code (albedo, thermal bridge factor, and convective coefficient). The validation is carried out by testing the goodness of fit of the code with respect to the field measurements, and then propagating the posterior parametric uncertainty through the code, obtaining probabilistic forecasts of the average electrical power delivered inside the cell in a given period of time. Finally, Bayesian decision-making methods are used to choose the optimal fixed rate (for the energy provider) of the contract, in order to balance short-term benefits with customer retention. We identify three significant contributions of the paper. First of all, the case study data were never analyzed from a Bayesian viewpoint, which is relevant here not only for estimating the parameters but also for properly assessing the uncertainty about the forecasts. Furthermore, the study of optimal policies for energy providers in this framework is new, to the best of our knowledge. Finally, we propose Bayesian posterior predictive p-value for validation.

Suggested Citation

  • Merlin Keller & Guillaume Damblin & Alberto Pasanisi & Mathieu Schumann & Pierre Barbillon & Fabrizio Ruggeri, 2022. "Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing," Post-Print hal-04071903, HAL.
    • Handle: RePEc:hal:journl:hal-04071903
    DOI: 10.3390/econometrics10040034
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-04071903v1
    as

    Download full text from publisher

    File URL: https://hal.inrae.fr/hal-04071903v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.3390/econometrics10040034?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
    ---><---

    References listed on IDEAS

    as
    1. Yen-chang Chang & Wen-liang Hung, 2007. "LINEX Loss Functions with Applications to Determining the Optimum Process Parameters," Quality & Quantity: International Journal of Methodology, Springer, vol. 41(2), pages 291-301, April.
    2. Cox, Dennis D. & Park, Jeong-Soo & Singer, Clifford E., 2001. "A statistical method for tuning a computer code to a data base," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 77-92, July.
    3. Fonseca, Jimeno A. & Nevat, Ido & Peters, Gareth W., 2020. "Quantifying the uncertain effects of climate change on building energy consumption across the United States," Applied Energy, Elsevier, vol. 277(C).
    4. Pasanisi, Alberto & Keller, Merlin & Parent, Eric, 2012. "Estimation of a quantity of interest in uncertainty analysis: Some help from Bayesian decision theory," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 93-101.
    5. Campbell, Katherine, 2006. "Statistical calibration of computer simulations," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1358-1363.
    6. Wate, P. & Iglesias, M. & Coors, V. & Robinson, D., 2020. "Framework for emulation and uncertainty quantification of a stochastic building performance simulator," Applied Energy, Elsevier, vol. 258(C).
    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. Merlin Keller & Guillaume Damblin & Alberto Pasanisi & Mathieu Schumann & Pierre Barbillon & Fabrizio Ruggeri & Eric Parent, 2022. "Validation of a Computer Code for the Energy Consumption of a Building, with Application to Optimal Electric Bill Pricing," Econometrics, MDPI, vol. 10(4), pages 1-24, November.
    2. Weiyan Mu & Chengxin Liu & Shifeng Xiong, 2023. "Nested Maximum Entropy Designs for Computer Experiments," Mathematics, MDPI, vol. 11(16), pages 1-12, August.
    3. Shi, Peng & Wang, Lin-Shu & Schwartz, Paul & Hofbauer, Peter, 2020. "State-wide comparative analysis of the cost saving potential of Vuilleumier heat pumps in residential houses," Applied Energy, Elsevier, vol. 277(C).
    4. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    5. Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.
    6. Guan, Zepeng & Hossain, Mohammad Razib & Sheikh, Muhammad Ramzan & Khan, Zeeshan & Gu, Xiao, 2023. "Unveiling the interconnectedness between energy-related GHGs and pro-environmental energy technology: Lessons from G-7 economies with MMQR approach," Energy, Elsevier, vol. 281(C).
    7. Mike G. Tsionas, 2023. "Linex and double-linex regression for parameter estimation and forecasting," Annals of Operations Research, Springer, vol. 323(1), pages 229-245, April.
    8. Chang, Yen-Chang & Liu, Ching-Ti & Hung, Wen-Liang, 2009. "Optimization of process parameters using weighted convex loss functions," European Journal of Operational Research, Elsevier, vol. 196(2), pages 752-763, July.
    9. Perrin, G., 2020. "Adaptive calibration of a computer code with time-series output," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    10. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
    11. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "Impact of adjustment strategies on building design process in different climates oriented by multiple performance," Applied Energy, Elsevier, vol. 266(C).
    12. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
    13. Alvaro Llaria & Jessye Dos Santos & Guillaume Terrasson & Zina Boussaada & Christophe Merlo & Octavian Curea, 2021. "Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management," Energies, MDPI, vol. 14(9), pages 1-37, May.
    14. Hjelkrem, Anne-Grete Roer & Höglind, Mats & van Oijen, Marcel & Schellberg, Jürgen & Gaiser, Thomas & Ewert, Frank, 2017. "Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments," Ecological Modelling, Elsevier, vol. 359(C), pages 80-91.
    15. Hong, H.P., 2013. "Selection of regressand for fitting the extreme value distributions using the ordinary, weighted and generalized least-squares methods," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 71-80.
    16. Yukai Zou & Zhuoxi Chen & Jialiang Guo & Yingsheng Zheng & Xiaolin Yang, 2024. "A data-driven framework for fast building energy demand estimation across future climate conditions," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 19, pages 628-641.
    17. Kim, Wongon & Yoon, Heonjun & Lee, Guesuk & Kim, Taejin & Youn, Byeng D., 2020. "A new calibration metric that considers statistical correlation: Marginal Probability and Correlation Residuals," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    18. Luka Pajek & Mitja Košir, 2021. "Exploring Climate-Change Impacts on Energy Efficiency and Overheating Vulnerability of Bioclimatic Residential Buildings under Central European Climate," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    19. Wilkinson Richard David, 2013. "Approximate Bayesian computation (ABC) gives exact results under the assumption of model error," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(2), pages 129-141, May.
    20. Amin Nouri & Christoph van Treeck & Jérôme Frisch, 2024. "Sensitivity Assessment of Building Energy Performance Simulations Using MARS Meta-Modeling in Combination with Sobol’ Method," Energies, MDPI, vol. 17(3), pages 1-24, January.

    More about this item

    Keywords

    uncertainty quantification; Bayesian analysis; energy contracts; uncertainty quantification Bayesian analysis energy contracts;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hal:journl:hal-04071903. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.