E3S Web Conf.
Volume 252, 20212021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|Number of page(s)||4|
|Section||Energy Technology Research and Development and Green Energy-Saving Applications|
|Published online||23 April 2021|
Analysis on Influencing Parameters of Heating Consumption Prediction
School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
* Corresponding author’s e-mail: firstname.lastname@example.org
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%.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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