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Stochastic Generation of District Heat Load

Author

Listed:
  • Andrea Menapace

    (Faculty of Science and Technology, Free University of Bozen-Bolzano, Universitätsplatz 5, 39100 Bolzano, Italy)

  • Simone Santopietro

    (Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, Italy)

  • Rudy Gargano

    (Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Via G. Di Basio 43, 03043 Cassino, Italy)

  • Maurizio Righetti

    (Faculty of Science and Technology, Free University of Bozen-Bolzano, Universitätsplatz 5, 39100 Bolzano, Italy)

Abstract

Modelling heat load is a crucial challenge for the proper management of heat production and distribution. Several studies have tackled this issue at building and urban levels, however, the current scale of interest is shifting to the district level due to the new paradigm of the smart system. This study presents a stochastic procedure to model district heat load with a different number of buildings aggregation. The proposed method is based on a superimposition approach by analysing the seasonal component using a linear regression model on the outdoor temperature and the intra-daily component through a bi-parametric distribution of different times of the day. Moreover, an empirical relationship, that estimates the demand variation given the average demand together with a user aggregation coefficient, is proposed. To assess the effectiveness of the proposed methodology, the study of a group of residential users connected to the district heating system of Bozen-Bolzano is carried out. In addition, an application on a three-day prevision shows the suitability of this approach. The final purpose is to provide a flexible tool for district heat load characterisation and prevision based on a sample of time series data and summary information about the buildings belonging to the analysed district.

Suggested Citation

  • Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5344-:d:623751
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    References listed on IDEAS

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    3. Ntumba Marc-Alain Mutombo & Bubele Papy Numbi, 2022. "Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load," Energies, MDPI, vol. 15(14), pages 1-20, July.

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