Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model

Zheng, Yunan; Luo, Juan; Chen, Jinbiao; Chen, Z C; Shang, Pengjian

doi:10.1016/j.jenvman.2023.118446

Cited by 16 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al (2019) investigated the correlation between Henry Hub natural gas prices and WTI crude oil prices through a multi-scale perspective. Zheng et al (2023) used the feature select algorithm to screen 20 variables affecting natural gas prices. It found that natural gas drilling activities and natural gas import and export prices had the most impact.…”

Section: Initial Variable Selectionmentioning

confidence: 99%

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Duan,

Zhang,

Wang

2023

Front. Energy Res.

View full text Add to dashboard Cite

As a clean fossil energy source, natural gas plays a crucial role in the global energy transition. Forecasting natural gas prices is an important area of research. This paper aims at developing a novel hybrid model that contributes to the prediction of natural gas prices. We develop a novel hybrid model that combines the “Decomposition Algorithm” (CEEMDAN), “Ensemble Algorithm” (Bagging), “Optimization Algorithm” (HHO), and “Forecasting model” (SVR). The hybrid model is used for monthly Henry Hub natural gas forecasting. To avoid the problem of data leakage caused by decomposing the whole time series, we propose a rolling decomposition algorithm. In addition, we analyzed the factors affecting Henry Hub natural gas prices for multivariate forecasting. Experimental results indicate that the proposed model is more effective than the traditional model at predicting natural gas prices.

show abstract

Section: Initial Variable Selectionmentioning

confidence: 99%

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Duan,

Zhang,

Wang

2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Combined with the Genetic Algorithm (GA) to solve the optimal solution of the design variables, GA is a probabilistic optimization method that combines the genetics of nature and computer science, which is based on the principle of biological evolution, following the idea of "survival of the fittest" in biology [45], and its benefits include excellent adaptability, stability, quick convergence, and more [46]. The parameters of the GA are set as follows: population size is 100, crossover probability is 0.6, mutation probability is 0.01, and the maximum number of hereditary generations is 300.…”

Section: Srn Improving and Validationmentioning

confidence: 99%

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

Jia,

Zhou,

Huang

et al. 2023

Electronics

View full text Add to dashboard Cite

In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state.

show abstract

“…Their model emphasizes the critical role of demand and supply fluctuations in predicting natural gas prices, highlighting the importance of incorporating macroeconomic indicators into forecasting models. Meanwhile, Zheng et al [9] took a different approach by examining the geopolitical impacts on natural gas pricing, specifically the effects of the Russia-Ukraine conflict. This study illustrates the necessity of integrating geopolitical factors into predictive models to account for external influences on market dynamics.…”

Section: Introductionmentioning

confidence: 99%

Advancing Natural Gas Price Predictions with ConcaveLSTM

Diqi,

Wanda,

Hamzah

et al. 2024

tech

View full text Add to dashboard Cite

This study investigates the application of the ConcaveLSTM model, a novel machine learning approach combining the strengths of Stacked Long Short-Term Memory (LSTM) and Bidirectional LSTM, for predicting natural gas prices. Given the inherent volatility and complexity of energy markets, accurate forecasting models are crucial for effective decision-making. The research employs a comprehensive dataset from 1997 to 2020, focusing on the daily price of natural gas in US Dollars per Million British thermal units (Btu). Through rigorous testing across various model configurations, the study identifies optimal settings for the ConcaveLSTM model that significantly improve prediction accuracy. Specifically, configurations utilizing 50 input steps with neuron counts of 100 and 300 exhibit superior performance, as evidenced by lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), alongside higher R-squared (R2) values. These findings validate the ConcaveLSTM model's potential in financial forecasting and highlight the importance of parameter tuning in enhancing model efficacy. Despite certain limitations regarding dataset scope and market variability, the results offer promising insights into developing advanced forecasting tools. Future research directions include expanding the dataset, incorporating additional market influencers, and conducting comparative analyses with other forecasting models. This study contributes to the evolving field of machine learning applications in financial market predictions, offering a foundation for further exploration and practical implementation in the energy sector.

show abstract

Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model

Cited by 16 publications

References 31 publications

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

Advancing Natural Gas Price Predictions with ConcaveLSTM

Contact Info

Product

Resources

About