A big-data-based recurrent neural network method for forest energy estimation

Abstract

In this paper, the recurrent neural network algorithm of big data is used to conduct in-depth research and analysis on the estimation of forest energy, and the corresponding forestry construction and biomass energy industry development are proposed. Firstly, by introducing a non-negative variable, the bounded constrained underdetermined linear system is transformed into a time-varying system consisting of linear and non-linear equations. Meanwhile, the actual situation of industrial raw material supply of forest biomass is explored by considering the technical level of collection, transportation, and storage. Then, three different alternative scale scenarios are designed to explore the development dynamics of the forest biomass industry in Guizhou under different levels of development and to propose optimization schemes and policy recommendations accordingly. Finally, the simulation results show that in terms of energy efficiency, economic benefits, and environmental benefits, the proposed mehtod can achieve carbon dioxide emission reduction benefits of about 46.17% and increase the economic benefits of carbon dioxide trading by 35.69%.