About: Correction in heat prediction depending on the deviation of forecasts and a historical data with similar pattern of outside temperature     Goto   Sponge   NotDistinct   Permalink

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  • This article deals with the prediction of heat requirements for the urban agglomeration and problems linked with calculation which is based on historical data. In previous articles were described algorithms and subsequent experiments identifying heat needs. Algorithms focused on seeking the days with similar behavior. These algorithms encounter gaps in the expected pattern and pattern contained in historical data. Determining similarity of the days based on outdoor temperature compliance is fairly inappropriate. It is necessary to take into account many other factors. The primary limitation is the time interval where the day can be searched. With regard to building insulation and general energy saving efforts have shown that the use of records from far distance such as previous years is very limited. Absolute amount of energy and often also the consumption pattern significantly differ. These limitations are shrinking space for finding a perfect match. This article will show one possible way, which can be effectively used to increase the accuracy of the heat prediction based on similar day seeking algorithms. Besides the processing of the most suitable data in identification algorithm the system was extended to correct the calculated heat needs depending on the differences between predicted and measured values from the past.
  • This article deals with the prediction of heat requirements for the urban agglomeration and problems linked with calculation which is based on historical data. In previous articles were described algorithms and subsequent experiments identifying heat needs. Algorithms focused on seeking the days with similar behavior. These algorithms encounter gaps in the expected pattern and pattern contained in historical data. Determining similarity of the days based on outdoor temperature compliance is fairly inappropriate. It is necessary to take into account many other factors. The primary limitation is the time interval where the day can be searched. With regard to building insulation and general energy saving efforts have shown that the use of records from far distance such as previous years is very limited. Absolute amount of energy and often also the consumption pattern significantly differ. These limitations are shrinking space for finding a perfect match. This article will show one possible way, which can be effectively used to increase the accuracy of the heat prediction based on similar day seeking algorithms. Besides the processing of the most suitable data in identification algorithm the system was extended to correct the calculated heat needs depending on the differences between predicted and measured values from the past. (en)
Title
  • Correction in heat prediction depending on the deviation of forecasts and a historical data with similar pattern of outside temperature
  • Correction in heat prediction depending on the deviation of forecasts and a historical data with similar pattern of outside temperature (en)
skos:prefLabel
  • Correction in heat prediction depending on the deviation of forecasts and a historical data with similar pattern of outside temperature
  • Correction in heat prediction depending on the deviation of forecasts and a historical data with similar pattern of outside temperature (en)
skos:notation
  • RIV/70883521:28140/12:43868542!RIV13-MSM-28140___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED2.1.00/03.0089)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 128840
http://linked.open...ai/riv/idVysledku
  • RIV/70883521:28140/12:43868542
http://linked.open...riv/jazykVysledku
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  • Consumption, distribution, heat, similar day, prediction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [960E9F55A939]
http://linked.open...v/mistoKonaniAkce
  • Kos Island
http://linked.open...i/riv/mistoVydani
  • Kos
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  • Proceedings of the 16th WSEAS International Conference on Systems
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Dolinay, Viliam
  • Vašek, Lubomír
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • WSEAS Press (GR)
https://schema.org/isbn
  • 978-1-61804-108-1
http://localhost/t...ganizacniJednotka
  • 28140
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