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Accurate forecast of natural gas consumption is of significance for policy makers to formulate energy plans, save costs and improve energy structure. This study designs a novel three-parameter discrete direct grey model denoted as TDDGM, which overcomes existing modeling drawbacks about the accumulating generation for monotonous series. Moreover, the optimal initial condition of the proposed model is deduced by the ordinary least square method and the final recursive function is derived. Error checking method of TDDGM and five different numerical cases are introduced to verify the superiority and effectiveness of the new approach. The results show that TDDGM performs better than other several benchmark models in multiple tests. Finally, it is utilized to forecast China's natural gas consumption and the predicted results will maintain a steady upward trend, reaching 322.06 billion cubic meters in 2022. The research results have positive significance for enriching grey system theory and improving energy structure in China. [ABSTRACT FROM AUTHOR]

Neasu (Tibeto-Burman: China) exhibits a prefix that derives new coordinators from existing ones by elaborately changing their subcategorial properties. Prefixed and unprefixed coordinators are distinguished by the complement they take (±verbal, ±CoP) and the possibility of being stacked up at least twice (±stackable). A prefixed coordinator has two of these three features switched from "−" to "+", when compared with its unprefixed counterpart and thus see its ability to occur as the head of recursive coordination structures increased. The prefix ao- is an operator of recursion. [ABSTRACT FROM AUTHOR]

The multidimensional dynamic programming (MDP) algorithm is a traditional method used to solve cascade reservoir operation optimization (CROO) problems, but the high dimensionality called the curse of dimensionalitycannot be ignored. In order to alleviate this problem, this paper proposes a new MDP algorithm named multilayer nested multidimensional dynamic programming (MNDP), which is based on a multilayered, nested structure. MNDP is mainly used to deal with computer memory space and computation complexity problems of MDP in CROO, and its recursive equation of reverse recursion calculation and specific calculation steps are presented in detail. This paper takes the cascade reservoirs of the Li Xianjiang River in China as an example to solve the CROO problem with the proposed MNDP. By comparing with the dynamic programming with successive approximations (DPSA) method, MNDP presents better performance in terms of power generation and the assurance rate in wet, normal, dry, and average years. The global optimality of MNDP is validated by MDP, and the parallel computing results of MNDP are shown in a case study. The MNDP proposed in this paper can reduce not only the programming complexity of MDP, but also the storage of intermediate variables during calculation, thus effectively solving the curse of dimensionality of MDP in CROO and keeping the global convergence feature of MDP. [ABSTRACT FROM AUTHOR]