Author(s)

Wujian Rao ¹, Panlin Li ²

Affiliation(s)

¹ School of Mathematics, Yunnan Normal University, Kunming, 650500, China
² College of Mathematics and Physics, Xinjiang Agricultural University, Urumqi, 830052, China

Corresponding Author

Wujian Rao

ABSTRACT

In this work, we propose an improved grey prediction model, called the conformable fractional accumulation kernel regularized nonhomogeneous grey model(CFAKRNGM), for energy consumption forecasting, and using HOA to optimize the model parameters, the CFAKRNGM model shows significant advantages in data fitting and prediction accuracy. In order to verify the effectiveness of the model, this work compares the prediction results of the CFAKRNGM model and the KRNGM model through the data analysis of renewable energy consumption and natural gas consumption in China. To provide theoretical support and practical reference for China's energy production and consumption planning. The results explain that the CFAKRNGM model performs better than the KRNGM in dealing with complex and nonlinear time series data, and has higher prediction accuracy and reliability. 

KEYWORDS

Grey Model; Kernel Regularization; Hiking Optimization Algorithm; Energy Comsumption Forecasts

CITE THIS PAPER

Wujian Rao, Panlin Li, An improved kernel regularized nonhomogeneous grey model based on conformable fractional accumulation. Transactions on Computational and Applied Mathematics (2024) Vol. 4: 137-147. DOI: http://dx.doi.org/10.23977/tracam.2024.040118.

REFERENCES

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