A discrete GM(1,1)model based on probabilistic accumulation and its application to offshore gas production forecasting
Accurate prediction of marine natural gas production is of great practical signifi-cance for driving the development of marine equipment manufacturing industry,reshaping the structure of domestic energy supply system and promoting leading scientific and technological research.Aiming at the shortcomings of the grey information superposition modeling approach in characterizing the operational properties of the system,this paper proposes a discrete GM(1,1)model based on probabilistic accumulation(PDGM(1,1)).First,the proposed model relies on the probabilistic accumulation operator to screen and extract the grey effective information,and deeply excavate the behavioral laws of the grey system operation;Second,the applicability of the proposed model to small sample data sets is demonstrated using matrix perturbation theory,which ensures the predictive advantage for small sample data;Third,for the problem of solv-ing complex information parameters in the proposed model,a model solving framework based on heuristic algorithms is proposed;Then,numerical simulation experiments were designed to simulate the nonlinear data environment to test the modeling and prediction capabilities of the PDGM(1,1)model.Meanwhile,real cases are introduced to verify the robustness and gener-alizability of the PDGM(1,1)model;Finally,the PDGM(1,1)model is applied to predict the offshore gas production.The results of this research,firstly,refined the information base of the grey prediction model modeling and proposed a new prediction modeling method,which is of positive significance for enriching and improving the system of prediction modeling methods;Secondly,the results of the offshore gas production forecast will provide a reference basis for the implementation of the path to expand China's marine economic development space under the new development pattern.
NETL
NSTL
万方数据
