Forecasting natural gas consumption with multiple seasonal patterns

Jia Ding, Yuxuan Zhao and Junyang Jin

Applied Energy, 2023, vol. 337, issue C, No S0306261923002751

Abstract: Natural gas is vital in the world’s energy portfolio and is widely applied to power generation, urban heating, and manufacturing. Forecasting natural gas consumption with high accuracy is thus crucial in order to maintain a reliable supply for various applications. The demand for natural gas often exhibits different seasonal patterns regarding customers of different characteristics. The precision of forecasters will be vulnerably affected without carefully exploring the periodicity of usage. This paper proposes a novel method, Dual Convolution with Seasonal Decomposition Network, for natural gas consumption forecasting. The proposed method applies multiple seasonal-trend decomposition to separate time series into periodic patterns and residual components. In addition, local and global convolution are combined to predict series with significant fluctuations and poor periodicity. Simulations show that on city-level forecasting, the proposed method outperforms state-of-the-art methods in terms of overall prediction accuracy and variation sensitivity regardless of different time intervals. The performance of the method is robust to the forecasting horizon. The method can be deployed in practical circumstances to forecast the natural gas consumption of residential quarters, cities, or even countries in different time spans.

Keywords: Natural gas consumption forecasting; Seasonal decomposition; Neural networks; CNN; Autoregressive (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923002751
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:337:y:2023:i:c:s0306261923002751

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.120911

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().