Machine Learning and Forecasting Approaches for Predicting Natural Gas Consumption

This paper explores the application of machine learning (ML) and forecasting algorithms for predictive modeling of natural gas usage. The study investigates various methodologies to effectively predict natural gas consumption patterns, leveraging historical data and meteorological factors as predictors. Key ML techniques such as Bidirectional Long Short-Term Memory (BiLSTM), Long Short-Term Memory (LSTM) and other forecasting techniques such as Facebook's Prophet, and the Holt-Winters Method (HWM) algorithm are employed in combined manner to develop accurate models capable of forecasting future gas demand. The results demonstrate the efficacy of these approaches in enhancing predictive accuracy, offering insights into optimizing resource allocation and energy management strategies. This research contributes to the advancement of predictive modeling in the energy sector, highlighting the potential for improving efficiency and sustainability in natural gas usage.