IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v34y2009i10p1447-1453.html
   My bibliography  Save this article

Simulation models for the analysis of space heat consumption of buildings

Author

Listed:
  • Popescu, Daniela
  • Ungureanu, Florina
  • Hernández-Guerrero, Abel

Abstract

This study develops and analyzes an original methodology for the simulation and prediction of space heating energy consumption in buildings connected to a district heating system, characterized by lack of individual control systems for end-users. The identification of the input parameters is based on both classical engineering equations and statistical analysis of collected data. Two main factors play important roles in the model: (i) climate and (ii) human behavior. Model validation was undertaken through the analysis of field data collected during the winter, via a monitoring system working in a partially-controlled district heating system. The comparison between the results obtained with the proposed model versus classical methods points out the possibility to implement, using the proposed methodology, management policies for a district that offer significant cost-effective energy savings opportunities.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:10:p:1447-1453
    DOI: 10.1016/j.energy.2009.05.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544209001972
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2009.05.035?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    2. Sørensen Torekov, Mikkel & Bahnsen, Niels & Qvale, Bjørn, 2007. "The relative competitive positions of the alternative means for domestic heating," Energy, Elsevier, vol. 32(5), pages 627-633.
    3. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    4. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    5. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    6. Heller, A. J., 2002. "Heat-load modelling for large systems," Applied Energy, Elsevier, vol. 72(1), pages 371-387, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Keçebaş, Ali & Alkan, Mehmet Ali & Yabanova, İsmail & Yumurtacı, Mehmet, 2013. "Energetic and economic evaluations of geothermal district heating systems by using ANN," Energy Policy, Elsevier, vol. 56(C), pages 558-567.
    3. Verda, Vittorio & Colella, Francesco, 2011. "Primary energy savings through thermal storage in district heating networks," Energy, Elsevier, vol. 36(7), pages 4278-4286.
    4. Sun, Chunhua & Chen, Jiali & Cao, Shanshan & Gao, Xiaoyu & Xia, Guoqiang & Qi, Chengying & Wu, Xiangdong, 2021. "A dynamic control strategy of district heating substations based on online prediction and indoor temperature feedback," Energy, Elsevier, vol. 235(C).
    5. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    6. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    7. 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.
    8. Hansen, Kenneth & Connolly, David & Lund, Henrik & Drysdale, David & Thellufsen, Jakob Zinck, 2016. "Heat Roadmap Europe: Identifying the balance between saving heat and supplying heat," Energy, Elsevier, vol. 115(P3), pages 1663-1671.
    9. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
    10. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    11. Prasad, Ravita D. & Bansal, R.C. & Raturi, Atul, 2014. "Multi-faceted energy planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 686-699.
    12. Difs, Kristina & Bennstam, Marcus & Trygg, Louise & Nordenstam, Lena, 2010. "Energy conservation measures in buildings heated by district heating – A local energy system perspective," Energy, Elsevier, vol. 35(8), pages 3194-3203.
    13. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    14. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    15. Niu, Shu-wen & Li, Yi-xin & Ding, Yong-xia & Qin, Jing, 2010. "Energy demand for rural household heating to suitable levels in the Loess Hilly Region, Gansu Province, China," Energy, Elsevier, vol. 35(5), pages 2070-2078.
    16. Gouveia, João Pedro & Fortes, Patrícia & Seixas, Júlia, 2012. "Projections of energy services demand for residential buildings: Insights from a bottom-up methodology," Energy, Elsevier, vol. 47(1), pages 430-442.

    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. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    2. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    3. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    4. Jae-Ki Byun & Young-Don Choi & Jong-Keun Shin & Myung-Ho Park & Dong-Kurl Kwak, 2012. "Study on the Development of an Optimal Heat Supply Control Algorithm for Group Energy Apartment Buildings According to the Variation of Outdoor Air Temperature," Energies, MDPI, vol. 5(5), pages 1-19, May.
    5. Pao, Hsiao-Tien, 2009. "Forecast of electricity consumption and economic growth in Taiwan by state space modeling," Energy, Elsevier, vol. 34(11), pages 1779-1791.
    6. Cinar, Didem & Kayakutlu, Gulgun & Daim, Tugrul, 2010. "Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey," Energy, Elsevier, vol. 35(4), pages 1724-1729.
    7. Federico Scarpa & Vincenzo Bianco, 2017. "Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector," Energies, MDPI, vol. 10(11), pages 1-13, November.
    8. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    9. Dahl, Magnus & Brun, Adam & Andresen, Gorm B., 2017. "Decision rules for economic summer-shutdown of production units in large district heating systems," Applied Energy, Elsevier, vol. 208(C), pages 1128-1138.
    10. Difs, Kristina & Danestig, Maria & Trygg, Louise, 2009. "Increased use of district heating in industrial processes - Impacts on heat load duration," Applied Energy, Elsevier, vol. 86(11), pages 2327-2334, November.
    11. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).
    12. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    13. Afshar, K. & Bigdeli, N., 2011. "Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)," Energy, Elsevier, vol. 36(5), pages 2620-2627.
    14. Cai, Yuan & Wang, Jian-zhou & Tang, Yun & Yang, Yu-chen, 2011. "An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network) neural network," Energy, Elsevier, vol. 36(2), pages 1340-1350.
    15. Zahedi, Gholamreza & Azizi, Saeed & Bahadori, Alireza & Elkamel, Ali & Wan Alwi, Sharifah R., 2013. "Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada," Energy, Elsevier, vol. 49(C), pages 323-328.
    16. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    17. Sahraei-Ardakani, Mostafa & Blumsack, Seth & Kleit, Andrew, 2015. "Estimating zonal electricity supply curves in transmission-constrained electricity markets," Energy, Elsevier, vol. 80(C), pages 10-19.
    18. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    19. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    20. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.

    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:eee:energy:v:34:y:2009:i:10:p:1447-1453. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.