Forecast Natural Gas Price by an Extreme Learning Machine Framework Based on Multi-Strategy Grey Wolf Optimizer and Signal Decomposition

Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price series makes it difficult to capture. Therefore, we p...

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Veröffentlicht in:Sustainability Jg. 17; H. 12; S. 5249
Hauptverfasser: Wu, Zhuolin, Zhou, Jiaqi, Yu, Xiaobing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2025
Schlagworte:
Accuracy
Decomposition (Mathematics)
Energy
Forecasting
Forecasts and trends
Gasoline prices
Machine learning
Mathematical optimization
Natural gas
Natural gas prices
Neural networks
Optimization algorithms
Prices and rates
Time series
China
ISSN:2071-1050, 2071-1050
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Zusammenfassung:Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price series makes it difficult to capture. Therefore, we propose a forecasting framework based on signal decomposition and intelligent optimization algorithms to predict natural gas prices. In this forecasting framework, we implement point, probability interval, and quantile interval forecasting. First, the natural gas price sequence is decomposed into multiple Intrinsic Mode Functions (IMFs) using the Ensemble Empirical Mode Decomposition (EEMD) technique. Each decomposed sequence is then predicted using an optimized Extreme Learning Machine (ELM), and the individual results are aggregated as the final result. To improve the efficiency of the intelligent algorithm, a Multi-Strategy Grey Wolf Optimizer (MSGWO) is developed to optimize the hidden layer matrices of the ELM. The experimental results prove that the proposed framework not only provides more reliable point forecasts with good nonlinear adaptability but also describes the uncertainty of natural gas price series more accurately and completely.
Bibliographie:ObjectType-Article-1
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ISSN:2071-1050
2071-1050
DOI:10.3390/su17125249