Daily natural gas load forecasting based on a hybrid deep learning model

35Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

References Powered by Scopus

Deep learning

60710Citations
N/AReaders
Get full text

Interpretation of the Correlation Coefficient: A Basic Review

1805Citations
N/AReaders
Get full text

LSTM network: A deep learning approach for Short-term traffic forecast

1397Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Conventional models and artificial intelligence-based models for energy consumption forecasting: A review

187Citations
N/AReaders
Get full text

Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm

173Citations
N/AReaders
Get full text

A Novel Evolutionary-Based Deep Convolutional Neural Network Model for Intelligent Load Forecasting

146Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Wei, N., Li, C., Duan, J., Liu, J., & Zeng, F. (2019). Daily natural gas load forecasting based on a hybrid deep learning model. Energies, 12(2). https://doi.org/10.3390/en12020218

Readers over time

‘19‘20‘21‘22‘240481216

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 7

58%

Lecturer / Post doc 5

42%

Readers' Discipline

Tooltip

Computer Science 6

60%

Engineering 2

20%

Energy 1

10%

Nursing and Health Professions 1

10%

Save time finding and organizing research with Mendeley

Sign up for free
0