Modelling Annual Natural Gas Demand Forecasting Using Non-Linear Autoregressive with Exogenous Input (NARX) Neural Networks

DOI: 10.14800/IOGR.1326

Abstract

Accurate natural gas demand forecasting is critical for ensuring efficient resource allocation, infrastructure planning, and energy security. This study presents the implementation of a NARX artificial neural network (ANN) model using MATLAB (R2022b) to forecast Nigeria’s natural gas demand. The NARX model, known for its capability to handle nonlinear time series data with external inputs, was applied using key variables such as population, GDP per capita, natural gas reserves, and price, with the target output being natural gas demand. The methodology involved data sourcing, cleaning, and normalization, followed by model training with the Levenberg-Marquardt (LM) algorithm in MATLAB, validation, and testing. Three different NARX configurations (NARX-1, NARX-2, and NARX-3) were tested, with sensitivity analyses conducted on the number of time delays and neurons to optimize the model's structure. Performance was evaluated using metrics like mean squared error (MSE) and coefficient of determination (R2), with results indicating that the NARX-1 model with 20 neurons achieved the best performance, boasting an R2 of 0.988. The result showed that natural gas demand in Nigeria has steadily increased over time, with fluctuations in response to global economic crises like the 2008 recession and the COVID-19 pandemic. Sensitivity analyses revealed that the NARX-1 configuration, with 20 neurons, provided the most accurate forecasting results based on its low MSE of 0.003396 and high R² value of 0.988155, outperforming other models. These findings demonstrate the effectiveness of the NARX model for forecasting natural gas demand, making it a valuable tool for energy planning and decision-making in Nigeria.