Abstract
Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.
Highlights
Global warming has a critical problem in countries all over the world, seriously threatening the development and survival of human beings especially in recent years.most countries have started to explore clean and low-greenhouse gas energy transitions and are beginning to decrease greenhouse gas emissions [1]
The seasonal ARIMA (p, d, q) * (P, D, Q)s model is a development of ARIMA which was introduced to advance the performance of auto-regressive integrated moving average in modeling the seasonal series [42,43,44,45]
Step 2: The selection of order autoregressive model (AR) and MA in the model parameters are determined for use in seasonal and non-seasonal ACF and PACF diagrams using determined values of (p, d, q) * (P, D, Q)s parameters estimated for the SARIMA and Seasonal ARIMA with eXogenous Factors (SARIMAX) models
Summary
Global warming has a critical problem in countries all over the world, seriously threatening the development and survival of human beings especially in recent years. Different NG production and consumption prediction methods have been continuously applied These forecasting methods can be divided into: time series methods and machine learning methods. Time-series methods can be determined as natural relationships They are widely used in natural gas consumption forecasts [10,30,31,32,33,34]. Machine learning models can be determined, their relationships are usually non-natural They are widely applied in evaluating natural gas consumption [35,36,37]. Correlativity between the monthly NG production and consumption and seasonal exogenous variables was reliably predicted by SARIMAX (p, d, q) * (P, D, Q)s model to determine high forecast success. Based on these exogenous factors, the superiority in order accuracy was achieved
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