Research On Optimization Of Energy Consumption Structure In Hebei Province Based On Carbon Emission Constraints

With the rapid development of the national economy,the consumption of high-carbon energy such as coal and oil in the country is still high,and the emission of carbon dioxide is increasing day by day.A series of side effects caused by climate warming have begun to threaten people's normal living environment and healthy.How to control carbon emissions,clean green development has become an important topic of increasing concern.The energy consumption in Hebei Province is mainly based on high-carbon emissions,the proportion of new energy consumption is low,the energy consumption structure is very unreasonable.Rapid economic development and large-scale urbanization have generated a large amount of energy demand,which has led to an increasingly serious environmental pollution problem in Hebei Province.In order to realize the environmental and economic sustainable development of Hebei Province,it is urgent to optimize its energy consumption structure.In this context,this paper first analyzes the energy status of Hebei Province through the energy balance table of Hebei Province and the energy consumption data of Hebei Province over the years.It is found that Hebei Province has low energy self-sufficiency rate,high proportion of coal consumption,and the proportion of clean energy and natural gas consumption.Low-level issues;Afterwards,this paper finds out the possible factors affecting energy consumption in Hebei Province,such as economic development,population,industrial structure,energy structure and technology level,and uses relevant analysis methods to prove that these factors are indeed very different from energy consumption in Hebei Province.The correlation coefficient between each influencing factor and energy consumption are all above 0.9;this paper introduces the deep learning method LSTM neural network model into the prediction of energy consumption,and uses the previously determined influencing factors as input vectors to construct energy consumption in Hebei Province.The prediction model is determined by adjusting the internal parameters of the LSTM model.After the parameters are determined,the model was tested 30 times.The average error of the test results is 0.703%,which indicates that the LSTM neural network model has higher prediction accuracy.Finally,based on the prediction results,a multi-objective optimization model of energy consumption structure in Hebei Province based on coordinated development of economy,environment and energy was constructed,which minimizes the cost of environmental pollution control and minimizes the cost of carbon emissions.The consumption of coal,oil,natural gas and primary electricity and other energy sources is used as a decision variable to solve the model.It clarifies the future development direction of energy structure in Hebei Province,and puts forward the optimization proposal for energy development in Hebei Province under the constraint of carbon emission,which is of great significance to the sustainable economic development of Hebei Province.