Analysis on Influencing Parameters of Heating Consumption Prediction

Analysis on Influencing Parameters of Heating Consumption Prediction

This paper studies the influencing parameters of the heating consumption prediction in heating substation, uses the BP neural network to predict the heating consumption, and establishes four BP neural network structures to change the outdoor average temperature, the lowest temperature and the highes...

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Main Authors: Tian Ye, Li Rui
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/28/e3sconf_pgsge2021_03024.pdf
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spelling doaj-f6f521839d45460a9aceaee92fcf98fc2021-05-04T12:18:57ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012520302410.1051/e3sconf/202125203024e3sconf_pgsge2021_03024Analysis on Influencing Parameters of Heating Consumption PredictionTian Ye0Li Rui1School of Environment and Energy Engineering, Beijing University of Civil Engineering and ArchitectureSchool of Environment and Energy Engineering, Beijing University of Civil Engineering and ArchitectureThis paper studies the influencing parameters of the heating consumption prediction in heating substation, uses the BP neural network to predict the heating consumption, and establishes four BP neural network structures to change the outdoor average temperature, the lowest temperature and the highest outdoor temperature, the predicted results found that when the input variables include the average outdoor temperature, the lowest outdoor temperature, the highest outdoor temperature, the wind speed, and the heating consumption of the previous three days, the prediction results are better , the relative error is equal or less than 0.25%.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/28/e3sconf_pgsge2021_03024.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Tian Ye
Li Rui
spellingShingle Tian Ye
Li Rui
Analysis on Influencing Parameters of Heating Consumption Prediction
E3S Web of Conferences
author_facet Tian Ye
Li Rui
author_sort Tian Ye
title Analysis on Influencing Parameters of Heating Consumption Prediction
title_short Analysis on Influencing Parameters of Heating Consumption Prediction
title_full Analysis on Influencing Parameters of Heating Consumption Prediction
title_fullStr Analysis on Influencing Parameters of Heating Consumption Prediction
title_full_unstemmed Analysis on Influencing Parameters of Heating Consumption Prediction
title_sort analysis on influencing parameters of heating consumption prediction
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2021-01-01
description This paper studies the influencing parameters of the heating consumption prediction in heating substation, uses the BP neural network to predict the heating consumption, and establishes four BP neural network structures to change the outdoor average temperature, the lowest temperature and the highest outdoor temperature, the predicted results found that when the input variables include the average outdoor temperature, the lowest outdoor temperature, the highest outdoor temperature, the wind speed, and the heating consumption of the previous three days, the prediction results are better , the relative error is equal or less than 0.25%.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/28/e3sconf_pgsge2021_03024.pdf
work_keys_str_mv AT tianye analysisoninfluencingparametersofheatingconsumptionprediction
AT lirui analysisoninfluencingparametersofheatingconsumptionprediction
_version_ 1721479117978730496

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