Forecasting Natural Gas Prices With Deep Learning and Compressed Sensing Denoising

Coal and oil have long been one of the major energy sources, but they contribute to significant carbon emissions and air pollution. Natural gas is an energy source that can help to achieve carbon neutrality and reduce air pollution, thereby achieving sustainable development. On the other hand, the ambiguity regarding the trajectory of natural gas prices can pose challenges in effectively controlling production expenses, introducing risks, and hindering the robust growth of the natural gas sector and its market. In this article, a hybrid model for natural price prediction is proposed, which combines compressed sensing-based denoising technology and deep learning (DL). Specifically, the model employs compressed sensing denosing as a preprocessor to extract pure data from the original data of natural gas spot price. Then, it is fed into a powerful DL model to generate accurate prediction results. These results underscore the efficacy of the suggested hybrid model in forecasting natural gas prices, offering valuable insights for informed decision-making, risk mitigation, and cost optimization in pursuit of sustainable development objectives.