Aiming at the issue that traditional multivariable grey prediction models cannot fully identify nonlinear trends among sequences, this paper introduces power-exponential and time-power-logarithmic correction terms, combines intelligent algorithms to flexibly optimize the model's unstructured parameters and proposes a new logarithmic flexible discrete grey LFDGM (1, N) model to deeply mine data sequence patterns and adapt to data development trends.
Firstly, nonlinear parameters are introduced into system behavior and relevant factor sequences, and a logarithmic time-power correction term is adopted to identify system nonlinear characteristics. Secondly, four mainstream intelligent optimization algorithms are compared to select the optimal one for hyperparameter global optimization with minimum average relative error. Then, the LFDGM (1, N) model is applied to fossil energy production prediction and compared with benchmark models. Finally, model stability is verified via Monte Carlo simulation and varying sample set experiments.
The experimental results show that the logarithmic flexible discrete grey LFDGM (1, N) model has better accuracy than other models in the global MAPE, verifying the validity and practicability of the model and indicating that this model has the optimal modeling effect on nonlinear complex systems. In addition, this paper verifies that the LFDGM (1, N) model has good robustness through Monte Carlo simulation and experiments with different sample sets.
Scientific and accurate prediction of fossil energy output is of great reference significance for China's formulation of energy industry plans and provides solid support for the long-term stable development of its energy industry.
To tackle complex, variable and highly uncertain system data sequences, this paper develops a logarithmic flexible discrete grey LFDGM (1, N) model. Embedding nonlinear power parameters in the system feature sequence and the related factor sequence, and introducing a logarithmic time-power correction term enhances nonlinear correlation depiction, captures early system abrupt changes and restricts late prediction divergence. Hyperparameters are optimized via preferred intelligent algorithms to improve prediction accuracy.
