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Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

The energy prices in Europe have in recent years surpassed unprecedented thresholds and varied in unexpected ways compared to previous years. This paper presents a study of the fuel markets in Italy, supplemented by insights from Sweden. Italy is heavily dependent on natural gas. The results show that natural gas demand changed only slightly in the period 2017–2022, but prices started to increase at the end of 2021. Notable spikes occurred at the beginning of the events in Ukraine, even though the baseline was already three times higher than the average price from 2017 to 2019. Distinct dynamics can be identified with the increase in demand for power generation, contrasted with a decrease in industrial natural gas demand after August 2022. The trends in coal and wood chip prices are consistent with those of natural gas, while oil prices appear to be less correlated. Additionally, events such as CO2 trading and the launch of the Fit for 55 program by the EU show some correlation with the trend in natural gas prices during 2021. Interestingly, the origin of the increase in natural gas prices during 2021–2022 cannot be simply attributed to the mismatch of supply and demand or any singular external event. This paper aims at starting a discussion on the topic by proposing some explanations.

The rationality of natural gas price has a significant impact on a nation's energy-saving strategies, emission reduction, and therefore the overall economy. This article examines the rationality of the natural gas pricing strategies in China through an investigation of the natural gas price distortions in domestic and international markets and the natural gas price determining factors using a Bayesian structural equation model. It is found that there are significant distortions in the industrial, residential, and commercial sectors compared to alternative energy sources. Further analysis suggests that these distortions could cause a \"reverse substitution\" in the domestic energy market. Compared to the international market, Chinese natural gas prices are found to be expensive and to lack price elasticity. The results also indicate that economic activities and demand are the most important natural gas price determinants, followed by supply and alternative fuel prices. This article provides empirical evidence to assist in natural gas pricing reforms in China and presents a basis for the design of reasonable energy pricing policies.

Natural gas is the main energy source and carbon emission source of integrated energy systems (IES). In existing studies, the price of natural gas is generally fixed, and the impact of price fluctuation which may be brought by future liberalization of the terminal side of the natural gas market on the IES is rarely considered. This paper constructs a natural gas price fluctuation model based on particle swarm optimization (PSO) and Dynamic Bayesian networks (DBN) algorithms. It uses the improved epsilon constraint method and fuzzy multi-weight technology to solve the Pareto frontier set considering the system operation cost and carbon emission. The system operation cost is described using Latin Hypercube Sampling (LHS) to predict the stochastic output of the renewable energy source, and a penalty function based on the Predicted Mean Vote (PMV) model to describe the thermal comfort of the user. This is analyzed using the Grey Wolf Optimization (GWO) algorithm. Carbon emissions are calculated using the carbon accounting method, and a ladder penalty mechanism is introduced to define the carbon trading price. Results of the comparison illustrate that the Pareto optimal solution tends to choose less carbon emission, electricity is more economical, and gas is less carbon-intensive in a small IES for end-users when the price of natural gas fluctuates. The impacts of various extents of natural gas price fluctuation for the same load are also discussed.

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.

Crude oil and natural gas are crucial to the Russian economy. Therefore, this study examined the interconnections between crude oil price, natural gas price, and Russian economic policy uncertainty (EPU) over the period 1994–2019 using multivariate DCC-MGARCH models. The findings show that there are strong interconnections (co-movement) between the energy prices and EPU in Russia, and that it might be misleading to assume independence or neutrality between the variables. Although Russia is also a crucial player in both the natural gas and the crude oil markets, this study reveals that there is a stronger co-movement of the EPU with gas price than with the oil price. Russia is the largest exporter of natural gas and the second-largest producer; it is plausible that the natural gas price correlates with EPU more than the crude oil price. Further, the correlation between gas price and EPU and the correlation between crude oil price and EPU have similar patterns. Each declines almost in the same period and, equally, increases concurrently. In addition, the results revealed that significant global shocks and crises, such as the 2008 global financial crisis, the 2014–2017 Russian financial crisis, the 9/11 terrorist attack, and the Russo–Ukrainian conflicts, influence the interconnections between the energy prices and Russian EPU.

Accurate natural gas price forecasting is vital for risk management, trading strategies, and policy-making in energy markets. This study proposes and evaluates four hybrid deep learning architectures—CNN-LSTM-Attention, CNN-BiLSTM-Attention, TCN-LSTM-Attention, and TCN-BiLSTM-Attention—integrating convolutional feature extraction, sequential learning, and attention mechanisms. Using Henry Hub and NYMEX datasets, the models are trained on long historical periods and tested under multi-step horizons. The results show that all hybrid models significantly outperform the traditional moving average benchmark, achieving R2 values above 95% for one-step-ahead forecasts and maintaining an accuracy of over 87% at longer horizons. CNN-BiLSTM-Attention performs best in short-term prediction due to its ability to capture bidirectional dependencies, while TCN-based models demonstrate greater robustness over extended horizons due to their effective modeling of long-range temporal structures. These findings confirm the advantages of deep learning hybrids in energy forecasting and emphasize the importance of horizon-sensitive evaluation. This study contributes methodological innovation and provides practical insights for market participants, with future directions including the integration of macroeconomic and climatic factors, exploration of advanced architectures such as Transformers, and probabilistic forecasting for uncertainty quantification.

Natural gas, a key low-emission energy source with significant strategic value in modern energy systems, necessitates accurate forecasting of its market price to ensure effective policy planning and economic stability. This paper proposes an ensemble framework to enhance natural gas price forecasting accuracy across multiple temporal scales (weekly and monthly) by constructing hybrid models and exploring diverse ensemble strategies, while balancing model complexity and computational efficiency. For weekly data, an Autoregressive Integrated Moving Average (ARIMA) model optimized via 5-fold cross-validation captures linear patterns, while the Long Short-Term Memory (LSTM) network captures nonlinear dependencies in the residual component after seasonal and trend decomposition based on LOESS (STL). For monthly data, the superior-performing model (ARIMA or SARIMA) is integrated with LSTM to address seasonality and trend characteristics. To further improve forecasting performance, three diverse ensemble techniques including stacking, bagging, and weighted averaging are individually implemented to synthesize the predictions of the two baseline models. The bagging ensemble method slightly outperforms other models on both weekly and monthly data, achieving MAPE, MAE, RMSE, and R2 values of 9.60%, 0.3865, 0.5780, and 0.8287 for the weekly data, and 11.43%, 0.5302, 0.6944, and 0.7813 for the monthly data, respectively. The accurate forecasting of natural gas prices is critical for energy market stability and the realization of sustainable development goals.

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.

With the characteristic of natural gas as a clean, non-toxic, and valuable energy source, its use has been increasing in recent years. Thus, maintaining stable natural gas security requires a reliable long-step price forecasting indicator with less error. We propose a hybrid theory of Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM) to perform multi-step forecasting focusing on 30 to 90 steps of the daily Henry Hub natural gas price as a dataset. Using four widespread error measurements, the proposed model provides excellent results compared to no-decomposition as the benchmark model. The proposed model provides 50% lower error results than the single LSTM. EEMD_LSTM brings values below 10 in the MAPE indicator, even up to 90-step prediction. The Diebold-Mariano test also confirms that EEMD_LSTM outperforms the single LSTM on every step with the majority of 90% confidence level. We also simulated the model by analysing the box and whiskers plot of RMSE, which shows that the variance of predicted values ranges between 1.11%. These results show that the proposed forecasting model provides robust results for the case of medium-term natural gas prices with excellent forecasting results.