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Research on Prediction Methods of Energy Consumption Data

Ning Chen1, Naernaer Xialihaer2,3, Weiliang Kong3, Jiping Ren2,3,*

1 Xinjiang Research Institute of Building Sciences (Co., Ltd.), Urumqi, 830000, China
2 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
3 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

* Corresponding Author: Jiping Ren. Email: email

Journal of New Media 2020, 2(3), 99-109. https://doi.org/10.32604/jnm.2020.09889

Abstract

This paper analyzes the energy consumption situation in Beijing, based on the comparison of common energy consumption prediction methods. Here we use multiple linear regression analysis, grey prediction, BP neural net-work prediction, grey BP neural network prediction combined method, LSTM long-term and short-term memory network model prediction method. Firstly, before constructing the model, the whole model is explained theoretically. The advantages and disadvantages of each model are analyzed before the modeling, and the corresponding advantages and disadvantages of these models are pointed out. Finally, these models are used to construct the Beijing energy forecasting model, and some years are selected as test samples to test the prediction accuracy. Finally, all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019, and the relevant energy-saving opinions were given.

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APA Style
Chen, N., Xialihaer, N., Kong, W., Ren, J. (2020). Research on prediction methods of energy consumption data. Journal of New Media, 2(3), 99-109. https://doi.org/10.32604/jnm.2020.09889
Vancouver Style
Chen N, Xialihaer N, Kong W, Ren J. Research on prediction methods of energy consumption data. J New Media . 2020;2(3):99-109 https://doi.org/10.32604/jnm.2020.09889
IEEE Style
N. Chen, N. Xialihaer, W. Kong, and J. Ren "Research on Prediction Methods of Energy Consumption Data," J. New Media , vol. 2, no. 3, pp. 99-109. 2020. https://doi.org/10.32604/jnm.2020.09889

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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