@Article{jnm.2020.09889,
AUTHOR = {Ning Chen, Naernaer Xialihaer, Weiliang Kong, Jiping Ren},
TITLE = {Research on Prediction Methods of Energy Consumption Data},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {99--109},
URL = {http://www.techscience.com/JNM/v2n3/40101},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2020.09889}
}