Authors :
Agabus Aminu; Fatima Umar Zambuk; Abdulsalam Ya’u Gital; Mustapha Abdulrahman Lawal; Yusuf Pyelshak; Ismail Zahraddeen Yakubu
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/3je2nm9e
DOI :
https://doi.org/10.5281/zenodo.8348415
Abstract :
The only byproducts of burning natural gas
are carbon dioxide, water vapor, and very little amounts
of nitrogen oxide, making it the cleanest fossil fuel on the
planet. A wide range of consumer products, such as
stoves, dryers, fireplaces, and furnaces, are also powered
by natural gas. At least one of your appliances
undoubtedly runs on natural gas. In this work, the
demand for residential natural gas was forecasted using a
hybrid ensemble regression machine learning approach.
Accurate forecasting of the demand for natural gas is
crucial for effective energy management and resource
allocation. The hybrid ensemble approach mixes a
number of regression algorithms, including linear
regression (LR), decision tree regression (DTR), support
vector regression (SVR), and K-nearest neighbor (KNN),
to take advantage of the benefits of each unique model
and improve prediction performance. The hybrid
ensemble regression model's process has two steps. In the
first stage, distinct regression models are trained on the
dataset. The second stage involves evaluating each
model's predictions. To evaluate the effectiveness of the
hybrid ensemble model, a range of measures, including
mean absolute error (MAE), mean squared error (MSE),
coefficient of determination (R-squared), and accuracy,
are generated and compared to those of individual
regression models. The anticipated accuracy of the model
is further assessed using cross-validation techniques to
ensure resilience. The results of the experiment
demonstrated that the hybrid ensemble regression
technique routinely outperformed individual regression
models in terms of prediction accuracy. Combining
numerous models enables the collection of the various
correlations and patterns contained in the data,
enhancing the model's overall performance.
Keywords :
Ensemble, Hybrid, Machine Learning, Natural Gas, Prediction.
The only byproducts of burning natural gas
are carbon dioxide, water vapor, and very little amounts
of nitrogen oxide, making it the cleanest fossil fuel on the
planet. A wide range of consumer products, such as
stoves, dryers, fireplaces, and furnaces, are also powered
by natural gas. At least one of your appliances
undoubtedly runs on natural gas. In this work, the
demand for residential natural gas was forecasted using a
hybrid ensemble regression machine learning approach.
Accurate forecasting of the demand for natural gas is
crucial for effective energy management and resource
allocation. The hybrid ensemble approach mixes a
number of regression algorithms, including linear
regression (LR), decision tree regression (DTR), support
vector regression (SVR), and K-nearest neighbor (KNN),
to take advantage of the benefits of each unique model
and improve prediction performance. The hybrid
ensemble regression model's process has two steps. In the
first stage, distinct regression models are trained on the
dataset. The second stage involves evaluating each
model's predictions. To evaluate the effectiveness of the
hybrid ensemble model, a range of measures, including
mean absolute error (MAE), mean squared error (MSE),
coefficient of determination (R-squared), and accuracy,
are generated and compared to those of individual
regression models. The anticipated accuracy of the model
is further assessed using cross-validation techniques to
ensure resilience. The results of the experiment
demonstrated that the hybrid ensemble regression
technique routinely outperformed individual regression
models in terms of prediction accuracy. Combining
numerous models enables the collection of the various
correlations and patterns contained in the data,
enhancing the model's overall performance.
Keywords :
Ensemble, Hybrid, Machine Learning, Natural Gas, Prediction.
