Abstract
Aiming to prevent from the imbalance between supply and demand of energy in which the share of emerging type is rapidly increasing, to predict the supply of emerging energy reliably is significant. However, the expected distribution uncertain and high-noise characteristics of emerging energy supply impede the reliable prediction. The Dual-LSTM (Long Short-Term Memory) model was constructed for the characteristic extracting and effective prediction of the expected distribution uncertain high-noise emerging energy supply time series. A case study on coal bed gas supply in China was conducted. Results showed that the Dual-LSTM model effectively solved the the problem of superfluous and non-quantifiable variables in the prediction of coal bed gas supply and extracted the statistical characteristics of expected distribution uncertain and high-noise data samples effectively with a relative error major less than 5% in short-term. Besides, the Dual-LSTM model has a significantly higher prediction accuracy while comparing with ARIMA model and original LSTM model. Ultimately, it is predicted that the year-on-year growth rates of coal bed gas supply of China from January to September, 2021, approximately maintains 75% in average based on the Dual-LSTM model. The Dual-LSTM model provides a reliable statistical model for policy decision to maintain national sustainability and stability.
Highlights
The imbalance between supply and demand of energy is regarded as the potential impact on the national energy security and operation
From the perspective of emerging energy supply which features expected distribution uncertain and high noise, deep learning model is desirable for whose regression analyse[33,34]
M represents the number of independent variables, n represents the dimension of independent variables, s represents the dimension of time series, e represents the epoch size of long short term memory (LSTM) model, h represents the units of hidden state of LSTM model, O() represents the time complexity function
Summary
The imbalance between supply and demand of energy is regarded as the potential impact on the national energy security and operation. Exponential smoothing model and ARIMA model are typical time series analysis methods for oil and gas production. The Grey model weakens the random factors of the original data by accumulating the original data time series It carries on the natural exponential regression to the data, and subsequently solves the parameters of the exponential function based on the least square method. Compared with Grey model and exponential smoothing model, ARIMA has better regression and prediction effect on expected distribution uncertain high noise time series. From the perspective of emerging energy supply which features expected distribution uncertain and high noise, deep learning model is desirable for whose regression analyse[33,34]. M represents the number of independent variables, n represents the dimension of independent variables, s represents the dimension of time series, e represents the epoch size of LSTM model, h represents the units of hidden state of LSTM model, O() represents the time complexity function
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
