The grey Theta forecasting model and its application to forecast primary energy consumption in major industrial countries

ABSTRACT

In this paper, a new grey Theta forecasting model is established to predict primary energy consumption. The parameter θ is used to adjust the slope of this trend. In addition, this hybrid method can be used in combination with other forms of grey model, which has great potential to improve the accuracy of prediction. The pseudo-code of the model is given, and the computational complexity of the proposed method is calculated. The robustness of the model is measured by the examples of primary energy consumption in China, the United States, India, Russian Federation and Japan. Numerical examples show that the proposed model is superior to other competitive models. Based on the local curvature adjustment of the Theta line, the proposed method can improve the predictive performance of the grey model in nonlinear trend data. Then, the future trends of global primary energy consumption and total carbon dioxide emissions are also forecasted. Predictions show that only China and India are expected to see annual primary energy demand rise by more than 5% over the next five years, while the United States, Japan and other countries will see no growth in demand for primary energy.

KEYWORDS:

• Theta line
• grey Theta forecasting model
• primary energy consumption
• non-homogeneous discrete grey model

Acknowledgments

We would like to thank the anonymous referees for their constructive comments. This work was supported by the National Natural Science Foundation of China (71871084).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71871084].

Notes on contributors

Gang Shang

Gang Shang is studying for a doctorate in the school of mechanical engineering at Tongji University. His research interests include machine learning, intelligent manufacturing and mining engineering.

Nu Li

Nu Li is a PhD student in management science and engineering at School of Economics and Management, China University of Petroleum Beijing. Dr. Li has published more than 10 journal  articles in high-impact journals, e.g., Energy, Journal of Cleaner Production. Her research interests include energy resource forecasting and assessment of coordinated effects of energy and environment.

Lianyi Liu

Lainyi Liu is a PhD scholar working in Nanjing University of Aeronautics and Astronautics. Dr. Liu has published many high-quality papers in international journals, such as  APPLIED MATHEMATICAL MODELLING, Communications in Nonlinear Science and Numerical Simulation.  His research interests are grey systems theory, time series prediction algorithms and energy systems research.