Advanced Deep Learning Techniques for Predicting Natural Gas Consumption a Comprehensive Forecasting Analysis

Natural gas consumption is influenced via several issues, including climatic conditions, commercial activity, and population growth. Accurate forecasting of both current and future demand is crucial for efficient energy management. Therefore, this work explores development of natural gas consumption forecasting techniques over the past decade and compares various forecasting systems. It focuses on the process of data preparation, exploration, visualization, and the application of Deep Learning (DL) methods. Specifically, the work focuses on utilizing advanced DL techniques, including the residual Deep Neural Network (DNN), for enhance forecast accuracy and efficiency. The implementation of the proposed model is carried out using Python, providing a flexible and scalable solution for real-world energy management applications. To evaluate model performance, key metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are employed, with results showing an RMSE of 2.1% and an MAE of 1.7%, demonstrating model's high analytical accuracy.