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

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  • F. Marta L. Di Lascio

    (Free University of Bozen-Bolzano)

  • Andrea Menapace

    (Free University of Bozen-Bolzano)

  • Maurizio Righetti

    (Free University of Bozen-Bolzano)

Abstract

This paper examines the complex dependence between peak district heating demand and outdoor temperature. Our aim is to provide the probability law of heat demand given extreme weather conditions, and derive useful implications for the management and production of thermal energy. We propose a copula-based approach and consider the case of the city of Bozen-Bolzano. The analysed data concern daily maxima heat demand observed from January 2014 to November 2017 and the corresponding outdoor temperature. We model the univariate marginal behaviour of the time series of heat demand and temperature with autoregressive integrated moving average models. Next, we investigate the dependence between the residuals’ time series through several copula models. The selected copula exhibits heavy-tailed and symmetric dependence. When taking into account the conditional behaviour of heat demand given extreme climatic events, the latter strongly affects the former, and we find a high probability of thermal energy demand reaching its peak.

Suggested Citation

  • F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 373-395, June.
    • Handle: RePEc:spr:stmapp:v:29:y:2020:i:2:d:10.1007_s10260-019-00488-4
    DOI: 10.1007/s10260-019-00488-4
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    Cited by:

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    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. 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.
    4. 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.

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    More about this item

    Keywords

    Copula function; Conditional probability; District heating system; Outdoor temperature; Peak heat demand; SARIMA models;
    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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