Open Access
ARTICLE
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:
Journal of New Media 2020, 2(3), 99-109. https://doi.org/10.32604/jnm.2020.09889
Received 17 January 2020; Accepted 31 January 2020; Issue published 04 September 2020
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.
Keywords
Cite This Article
N. Chen, N. Xialihaer, W. Kong and J. Ren, "Research on prediction methods of energy consumption data,"
Journal of New Media, vol. 2, no.3, pp. 99–109, 2020.
Citations