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ABSTRACT
Accurate prediction of oil and natural gas consumption (ONGC) is crucial for energy security and greenhouse gas emission control. This study uses machine learning to improve forecast accuracy by transforming time series predictions into supervised learning models. A novel stacking learning method, with added cross-validation, enhances model diversity and robustness. The key findings are: (1) The stacking model outperforms base models in predicting China’s ONGC. It achieves R2 scores of 94.44% for oil and 98.33% for natural gas, with corresponding RMSE scores of 0.5325 and 0.2919. (2) When comparing the scores of the models in the validation set using cross-validation, it can be observed that the stacking model exhibits the most consistent performance. (3) Through the diversification of models, the stacking approach enhances robustness and achieves better generalization on new datasets. The study provides fresh insights into model stacking for energy consumption prediction.
Abbreviations
| ONGC | = | oil and natural gas consumption |
| ARDL | = | autoregressive distributed lag |
| SD | = | system dynamics |
| ML | = | machine learning |
| LR | = | linear regression |
| LASSO | = | least absolute shrinkage and selection operator |
| SVR | = | support vector regression |
| LSTM | = | long short-term memory |
| GDP | = | gross domestic product |
| CI | = | carbon intensity |
| MTS | = | multivariate time series |
| OC | = | oil consumption |
| NGC | = | natural gas consumption |
| CO2 | = | carbon dioxide |
| K-CV | = | k-fold cross validation |
| R2 | = | R-squared |
| RMSE | = | root mean squared error |
| MAE | = | mean absolute error |
| DT | = | decision tree |
| RF | = | random forest |
| GBDT | = | gradient boosting decision trees |
| MS | = | mean scores |
| OECD | = | organization for economic co-operation and development |
| CASS | = | Chinese Academy of Social Sciences |
| DRCI | = | decline rate of carbon intensity |
| GGDP | = | growth rate of GDP |
| MT | = | million tons |
| BCM | = | billion cubic meters |
Availability of data and materials
The datasets used and analyzed during the current study can be provided on reasonable request.
Consent to publish
The manuscript is approved by all authors for publication.
Competing interests
The authors declare that they have no conflict of interest exits in the submission of this manuscript.
Ethical approval
The authors declare that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.