Estimating Residential Natural Gas Demand and Consumption: A Hybrid Ensemble Machine Learning Approach
DOI:
https://doi.org/10.26740/vubeta.v2i3.40135Keywords:
Estimation, Natural gas, Hybrid, Ensemble, Machine learningAbstract
The routine use of natural gas, particularly in residential settings, has been integral to human activities for many decades. This study proposes a hybrid ensemble regression machine learning model for forecasting residential natural gas demand. Accurate demand prediction is essential for efficient energy management and resource planning. The proposed approach integrates multiple regression algorithms including K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Linear Regression (LR) to leverage the strengths of each model and enhance overall predictive performance. The ensemble method operates in two phases: training individual regression models on the dataset, followed by aggregating their predictions. Model performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R²), and prediction accuracy, and is benchmarked against individual models. Cross-validation techniques were applied to ensure the robustness of the results. Experimental consequences demonstrate that the hybrid ensemble approach consistently outperforms standalone models by capturing diverse patterns and relationships within the data.
Author Biographies
Mohammed Ajuji, Department of Computer Science, Faculty of Science, Gombe State University (GSU), Gombe, Nigeria
Mohammed Ajuji is a lecturer in the Department of Computer Science, Faculty of Science, Gombe State University, Gombe, Nigeria. He received a Bachelor of Science (BSc) from the Gombe State University (GSU) in Computer Science. He also received a Master of Science (MSc) from the Abubakar Tafawa Balewa University (ATBU), Bauchi, Nigeria in 2023. He has published papers mainly in the fields of machine learning, artificial intelligence, and computer vision. He is currently undergoing his PhD program at the Modibbo Adama University, Yola. He can be contacted at email: majuji@gsu.edu.ng.
Muhammad Dawaki , Department of Computer Science, Faculty of Science, Gombe State University (GSU), Gombe, Nigeria
Muhammad Dawaki is a lecturer in the Department of Computer Science, at Gombe State University, Gombe, Nigeria. He received a Bachelor of Science (BSc) from the Gombe State University (GSU) in Computer Science, in 2015. He also received a Master of Science (MSc) from the University of Leeds in 2018. He is mainly researching data mining, artificial intelligence, and blockchain technology. He can be contacted at email: dawaki@gsu.edu.ng.
Ahmed Mohammed , Department of Computer Science, Faculty of Science, Gombe State University (GSU), Gombe, Nigeria
Department of Computer Science, Faculty of Science, Gombe State University (GSU), Gombe, Nigeria
Abuzairu Ahmad, Department of Mathematical Sciences, Abubakar Tafawa Balewa University (ATBU), Bauchi, 740272, Nigeria
Department of Mathematical Sciences, Abubakar Tafawa Balewa University (ATBU), Bauchi, 740272, Nigeria
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