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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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    1. Guixiang Xue & Yu Pan & Tao Lin & Jiancai Song & Chengying Qi & Zhipan Wang, 2019. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model," Energies, MDPI, vol. 12(11), pages 1-21, June.
    2. Protić, Milan & Shamshirband, Shahaboddin & Petković, Dalibor & Abbasi, Almas & Mat Kiah, Miss Laiha & Unar, Jawed Akhtar & Živković, Ljiljana & Raos, Miomir, 2015. "Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm," Energy, Elsevier, vol. 87(C), pages 343-351.
    3. 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.
    4. Gadd, Henrik & Werner, Sven, 2013. "Daily heat load variations in Swedish district heating systems," Applied Energy, Elsevier, vol. 106(C), pages 47-55.
    5. Lake, Andrew & Rezaie, Behanz & Beyerlein, Steven, 2017. "Review of district heating and cooling systems for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 417-425.
    6. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    7. Popescu, Daniela & Ungureanu, Florina & Hernández-Guerrero, Abel, 2009. "Simulation models for the analysis of space heat consumption of buildings," Energy, Elsevier, vol. 34(10), pages 1447-1453.
    8. Mathiesen, Brian Vad & Lund, Henrik & Karlsson, Kenneth, 2011. "100% Renewable energy systems, climate mitigation and economic growth," Applied Energy, Elsevier, vol. 88(2), pages 488-501, February.
    9. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    10. Lund, Henrik & Østergaard, Poul Alberg & Chang, Miguel & Werner, Sven & Svendsen, Svend & Sorknæs, Peter & Thorsen, Jan Eric & Hvelplund, Frede & Mortensen, Bent Ole Gram & Mathiesen, Brian Vad & Boje, 2018. "The status of 4th generation district heating: Research and results," Energy, Elsevier, vol. 164(C), pages 147-159.
    11. Sarwar, Riasat & Cho, Heejin & Cox, Sam J. & Mago, Pedro J. & Luck, Rogelio, 2017. "Field validation study of a time and temperature indexed autoregressive with exogenous (ARX) model for building thermal load prediction," Energy, Elsevier, vol. 119(C), pages 483-496.
    12. Mazhar, Abdur Rehman & Liu, Shuli & Shukla, Ashish, 2018. "A state of art review on the district heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 420-439.
    13. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    14. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    15. Guelpa, Elisa & Toro, Claudia & Sciacovelli, Adriano & Melli, Roberto & Sciubba, Enrico & Verda, Vittorio, 2016. "Optimal operation of large district heating networks through fast fluid-dynamic simulation," Energy, Elsevier, vol. 102(C), pages 586-595.
    16. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Analysing the relationship between district heating demand and weather conditions through conditional mixture copula," BEMPS - Bozen Economics & Management Paper Series BEMPS68, Faculty of Economics and Management at the Free University of Bozen.
    17. Siwei, Lang & Yu Joe Huang,, 1993. "Energy conservation standard for space heating in Chinese urban residential buildings," Energy, Elsevier, vol. 18(8), pages 871-892.
    18. Newsham, Guy R. & Donnelly, Cara L., 2013. "A model of residential energy end-use in Canada: Using conditional demand analysis to suggest policy options for community energy planners," Energy Policy, Elsevier, vol. 59(C), pages 133-142.
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    Cited by:

    1. 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.
    2. Liudmyla Davydenko & Nina Davydenko & Agnieszka Deja & Bogusz Wiśnicki & Tygran Dzhuguryan, 2023. "Efficient Energy Management for the Smart Sustainable City Multifloor Manufacturing Clusters: A Formalization of the Water Supply System Operation Conditions Based on Monitoring Water Consumption Prof," Energies, MDPI, vol. 16(11), pages 1-25, June.
    3. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2021. "A spatially-weighted AMH copula-based dissimilarity measure for clustering variables: An application to urban thermal efficiency," BEMPS - Bozen Economics & Management Paper Series BEMPS89, Faculty of Economics and Management at the Free University of Bozen.
    4. 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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