International Journal of Energy and Power Engineering

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Predicting Heat Demand for a District Heating Systems

Received: 31 August 2014    Accepted: 5 October 2014    Published: 20 October 2014
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Abstract

Poland is one of the heaviest users of district heating systems in Europe, and those district heating systems are heated mainly by coal. Sustainable development of district heating systems in Poland including improving quality of environment, economic of heat production and security of heat supply is in close connection with increasing of energy efficiency. Heat production and heat distribution plays important role in national energy balance. Additional increasing of energy efficiency in district heating systems need detail forecasts for future heat consumption in scale of individual district heating system and for systems in whole country. Accurate forecast give possibility for increasing efficiency of heat production, decreasing fuel consumption and connected with it emission decreasing from combustion products to the atmosphere. Heat production efficiency can be optimized through the use of appropriate procedures for running heat sources alongside short-term heat demand forecasting combined with preparation for adjusting heat source work parameters to the predicted heat load for a few hours hence. The artificial neural networks model delivers good forecasting results. The accuracy of the results depends on the kind of network, its architecture, the size and type of input data as well as the forecasting period. Forecasting accuracy within a 3-5% margin of error is sufficient to steer heat source operations. Described forecasting methods can be use as a good tool to regulate district heating networks and heat sources.

DOI 10.11648/j.ijepe.20140305.13
Published in International Journal of Energy and Power Engineering (Volume 3, Issue 5, October 2014)
Page(s) 237-244
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

District Heating Systems, Heat Demand Prediction

References
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    Krzysztof Wojdyga. (2014). Predicting Heat Demand for a District Heating Systems. International Journal of Energy and Power Engineering, 3(5), 237-244. https://doi.org/10.11648/j.ijepe.20140305.13

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    ACS Style

    Krzysztof Wojdyga. Predicting Heat Demand for a District Heating Systems. Int. J. Energy Power Eng. 2014, 3(5), 237-244. doi: 10.11648/j.ijepe.20140305.13

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    AMA Style

    Krzysztof Wojdyga. Predicting Heat Demand for a District Heating Systems. Int J Energy Power Eng. 2014;3(5):237-244. doi: 10.11648/j.ijepe.20140305.13

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  • @article{10.11648/j.ijepe.20140305.13,
      author = {Krzysztof Wojdyga},
      title = {Predicting Heat Demand for a District Heating Systems},
      journal = {International Journal of Energy and Power Engineering},
      volume = {3},
      number = {5},
      pages = {237-244},
      doi = {10.11648/j.ijepe.20140305.13},
      url = {https://doi.org/10.11648/j.ijepe.20140305.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20140305.13},
      abstract = {Poland is one of the heaviest users of district heating systems in Europe, and those district heating systems are heated mainly by coal. Sustainable development of district heating systems in Poland including improving quality of environment, economic of heat production and security of heat supply is in close connection with increasing of energy efficiency. Heat production and heat distribution plays important role in national energy balance. Additional increasing of energy efficiency in district heating systems need detail forecasts for future heat consumption in  scale of individual district heating system  and for systems in whole country. Accurate forecast give possibility for increasing  efficiency of heat production, decreasing fuel consumption and connected with it emission decreasing from combustion products to the atmosphere. Heat production efficiency can be optimized through the use of appropriate procedures for running heat sources alongside short-term heat demand forecasting combined with preparation for adjusting heat source work parameters to the predicted heat load for a few hours hence. The artificial neural networks model delivers good forecasting results. The accuracy of the results depends on the kind of network, its architecture, the size and type of input data as well as the forecasting period. Forecasting accuracy within a 3-5% margin of error is sufficient to steer heat source operations. Described forecasting methods can be use as a good tool to regulate district heating networks and heat sources.},
     year = {2014}
    }
    

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    T1  - Predicting Heat Demand for a District Heating Systems
    AU  - Krzysztof Wojdyga
    Y1  - 2014/10/20
    PY  - 2014
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    DO  - 10.11648/j.ijepe.20140305.13
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijepe.20140305.13
    AB  - Poland is one of the heaviest users of district heating systems in Europe, and those district heating systems are heated mainly by coal. Sustainable development of district heating systems in Poland including improving quality of environment, economic of heat production and security of heat supply is in close connection with increasing of energy efficiency. Heat production and heat distribution plays important role in national energy balance. Additional increasing of energy efficiency in district heating systems need detail forecasts for future heat consumption in  scale of individual district heating system  and for systems in whole country. Accurate forecast give possibility for increasing  efficiency of heat production, decreasing fuel consumption and connected with it emission decreasing from combustion products to the atmosphere. Heat production efficiency can be optimized through the use of appropriate procedures for running heat sources alongside short-term heat demand forecasting combined with preparation for adjusting heat source work parameters to the predicted heat load for a few hours hence. The artificial neural networks model delivers good forecasting results. The accuracy of the results depends on the kind of network, its architecture, the size and type of input data as well as the forecasting period. Forecasting accuracy within a 3-5% margin of error is sufficient to steer heat source operations. Described forecasting methods can be use as a good tool to regulate district heating networks and heat sources.
    VL  - 3
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Author Information
  • Faculty of Environmental Engineering, Warsaw University of Technology, Warsaw, Poland

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