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Joint and conditional dependence modeling of peak district heating demand and outdoor temperature: a copula-based approach

Author

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  • F. Marta L. Di Lascio

    (Free University of Bolzano‐Bozen, Faculty of Economics, Italy)

  • Andrea Menapace

    (Free University of Bolzano‐Bozen, Faculty of Science and Technology, Italy)

  • Maurizio Righetti

    (Free University of Bolzano‐Bozen, Faculty of Science and Technology, Italy)

Abstract

This paper examines the complex dependence between the peak district heating demand and the outdoor temperature. The final aim is to provide the probability law of the heat demand given extreme weather conditions and derive useful implications for the management and the production of thermal energy. We propose a copula-based approach and consider the case of the district of the city of Bozen-Bolzano. The analysed data concerns daily maxima of heat demand observed from January 2014 to November 2017 and the corresponding outdoor temperature. We find that the marginal behavior of the univariate time series of the district heating demand and the temperature is well-described by autoregressive integrated moving average models. Moreover, the selected copula model exhibits a symmetric dependence between the two investigated phenomena that tend to comove closely together during the whole heating season. Taking into account the conditional behaviour of the heat demand given the temperature leads to find that the demand is strongly affected by the temperature and, in case of extreme climatic events, the demand of thermal energy reach a peak with high probability. These findings motivate for improving the production schedule, the system design, and the operational strategies.

Suggested Citation

  • F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2018. "Joint and conditional dependence modeling of peak district heating demand and outdoor temperature: a copula-based approach," BEMPS - Bozen Economics & Management Paper Series BEMPS53, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps53
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    File URL: https://link.springer.com/article/10.1007/s10260-019-00488-4
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    Cited by:

    1. Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    3. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    4. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2021. "A spatially-weighted AMH copula-based dissimilarity measure to cluster variables in panel data," BEMPS - Bozen Economics & Management Paper Series BEMPS89, Faculty of Economics and Management at the Free University of Bozen.

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    More about this item

    Keywords

    ARIMA models; Copula function; Conditional probability; District heating system; Outdoor temperature; Peak heat demand;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment

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