Improved BP Neural Network of Heat Load Forecasting Based on Temperature and Date Type

Authors

Li Qi, College of Information Engineering, University of Science and Technology of Inner Mongol, Baotou 014000, China;
Zhao Feng, College of Information Engineering, University of Science and Technology of Inner Mongol, Baotou 014000, China;

Abstract

Abstract: The heat load forecasting provides data support for urban district heating systems, which is the basis of need-based heating. The change of heat load is greatly influenced by various exterior factors, especially the outdoor temperature. To meet demand of heating system, save energy and balance the comfort of human body, a kind of improved BP neural network method is proposed by temperature and date type. The temperature and date type are quantified and the heat load forecasting model is established by using BP neural network. To guarantee prediction accuracy, the genetic algorithm is used to optimize the weights and thresholds of the neural network, and from which the predicted value of the heat load in the next 24 hours is obtained. The predicted results from the model show that the proposed method can accurately predict the future heat load;and the goals of on-demand heating, energy conservation and environmental protection are achieved.

Recommended Citation

Qi, Li and Feng, Zhao (2019) "Improved BP Neural Network of Heat Load Forecasting Based on Temperature and Date Type," Journal of System Simulation: Vol. 30: Iss. 4, Article 31.
DOI: 10.16182/j.issn1004731x.joss.201804031
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss4/31

First Page

1464

Revised Date

2016-06-23

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201804031

Last Page

1472

CLC

TP301.6

Recommended Citation

Li Qi, Zhao Feng. Improved BP Neural Network of Heat Load Forecasting Based on Temperature and Date Type[J]. Journal of System Simulation, 2018, 30(4): 1464-1472.

DOI

10.16182/j.issn1004731x.joss.201804031