A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Abstract Natural gas load forecasting provides decision‐making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas l...

Full description

Bibliographic Details
Main Authors: Huibin Zeng, Bilin Shao, Genqing Bian, Hongbin Dai, Fangyu Zhou
Format: Article
Language:English
Published: Wiley 2022-07-01
Series:Energy Science & Engineering
Subjects:
GARCH family models
LSTM
natural gas load
volatility prediction
XGBoost
Online Access:https://doi.org/10.1002/ese3.1122

Similar Items

Similar Items