Optimizing Natural Gas Production Predictions: A Machine Learning Approach with Recurrent Neural Networks

Abstract—Forecasting natural gas production poses a significant challenge due to its intricate dynamics and critical implications for energy security and market operations. This study investigates the use of Recurrent Neural Networks (RNNs) in predicting total gas production in the United States, emphasizing the advantages of employing advanced artificial intelligence methodologies in time series forecasting. By evaluating the performance of RNNs against traditional Autoregressive Integrated Moving Average (ARIMA) models, the research reveals that RNNs achieve markedly better Root Mean Square Error (RMSE), demonstrating superior absolute accuracy. Conversely, ARIMA models show lower Mean Absolute Percentage Error (MAPE), reflecting higher percentage accuracy in predictions. These findings highlight the potential of RNNs to improve predictive accuracy in complex forecasting scenarios and suggest future research avenues, such as the development of hybrid models that leverage the complementary strengths of both RNNs and ARIMA. The study makes a valuable contribution to the academic literature on energy forecasting while providing actionable insights for industry professionals seeking to enhance production planning and market strategy optimization. Keyword: Recurrent Neural Networks, Natural Gas Production, ARIMA Models, Energy Sector Forecasting, Machine Learning