Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques

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Lumbreras_2020_IOP_Conf._Ser.__Earth_Environ._Sci._588_032007.pdf (753.12 KB)
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ISSN: 1755-1307
DOI: 10.1088/1755-1315/588/3/032007
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2020-11-20
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The use of smart energy meters enables the monitoring of large quantity of data related to heat consumption patterns in buildings connected to DH networks. This information can be used to understand the interaction between building and the final usersĀ“ without accurate information about building characteristics and occupational rates. In this paper an intuitive and clarifier data-driven model is presented, which couples heat demand and weather variables. This model enables the disaggregation of Space-Heating & Domestic Hot water demand, characterization of the total heat demand and the forecasting for the next hours. Simulations for 53 building have been carried out, with satisfactory results for most of them, reaching R2 values above 0.9 in some of them.
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Publisher Copyright: Ā© Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.
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Smart energy meters , Energy , Building , Smart energy meters , Energy , Building , General Environmental Science , General Earth and Planetary Sciences , SDG 7 - Affordable and Clean Energy , Project ID , info:eu-repo/grantAgreement/EC/H2020/768567/EU/REnewable Low TEmperature District/RELaTED , info:eu-repo/grantAgreement/EC/H2020/768567/EU/REnewable Low TEmperature District/RELaTED , Funding Info , This project has received funding from the European Unionā€™s Horizon 2020 research and innovation programme under grant agreement No 768567. , This project has received funding from the European Unionā€™s Horizon 2020 research and innovation programme under grant agreement No 768567.
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Lumbreras , M , Garay , R & Marijuan , A G 2020 , ' Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques ' , IOP Conference Series: Earth and Environmental Science , vol. 588 , no. 3 , 032007 , pp. 106-110 . https://doi.org/10.1088/1755-1315/588/3/032007