Focusing on the coupling relationship between industrial economy, energy power and carbon emissions, this paper combined the decomposition model, autoregressive model and nonlinear regression model of driving factors of carbon emissions to predict regional carbon emissions. The results show that the fitting effect of the combined model is significantly better than that of the single model, which can provide more accurate prediction for the regional carbon peak, and lay a solid foundation for the auxiliary promotion of regional carbon reduction.
As a result of the comprehensive development of new energy, the indicators used to assess the level of new energy development in a region involve a variety of aspects. The relatively redundant and complex data indicators and various factors with different impact scopes make it difficult to give an intuitive overall quantitative level of new energy development in a region, and the non-uniform evaluation indicators make the results obtained from different provinces or regions no longer comparable. Based on the principal component analysis method, this paper establishes a set of general and unified new energy development level indicator evaluation system for all provinces from quantitative analysis to qualitative analysis, which can intuitively and quickly evaluate the indicators and overall development level of major provinces or regions in a comprehensive way, thereby providing methods and practical basis for analyzing the distribution and laws of new energy development across the country.
With the rapid development of new energy vehicles, problems such as “charging difficulty” are becoming increasingly prominent. The charging space is occupied by fuel vehicles, resulting in the inability of new energy vehicles to charge after they arrive at the charging station. This paper proposes a license plate recognition algorithm model which combines the three steps of vehicle detection, license plate detection, and license plate recognition, which improves the robustness of license plate recognition in complex scenes. In view of the lack of Chinese license plate data, the data enhancement method is adopted to improve the recognition accuracy of Chinese provincial abbreviations and common color license plates such as blue, green, and yellow. According to the characteristics of a small license plate target and easy rotation in the license plate recognition task, the WPOD-NET network is selected for license plate detection. Finally, the license plates of new energy vehicles and fuel vehicles are distinguished by recognizing the color and law of license plates, so as to provide supporting information for the generation of fuel vehicle occupancy alarm. In this paper, the data collected by the cameras of some charging stations in a city are used for verification. The verification results show that the accuracy of oil vehicle occupancy recognition is as high as 97%.
Currently, the policy of energy system transformation represents a national strategy. For the development of the renewable energy industry, utility subsidies are required, and the forecast subsidy allocation amount continues to be an extremely important issue. Under this guide, we use a multiple linear regression algorithm to simulate and calculate the undetermined coefficients for wind power, photovoltaic power, capacity of biomass power projects, amounts of on-grid electricity, subsidy period, and the amount allocated by the governments as independent variables, respectively; and the amount to be allocated in the next year as the result. For example, the undetermined coefficients for wind power we calculated are -0.7579, 0.0747, 0.9664, 0.9134, and 47.863, respectively. We then put these undetermined coefficients into multiple linear regression, and obtain a new model for calculating energy subsidies of various types. The results indicate that multiple linear regression plays a significant role in the application of subsidy prediction, and provides a more reliable method for enterprises to estimate the number of subsidies allocated.
KEYWORDS: Carbon, Analytical research, Atmospheric modeling, Wind energy, Data modeling, Control systems, Renewable energy, Oxidation, Solar energy, Power supplies
The power industry is the pioneer of energy conservation and emission reduction in China, and energy conservation and emission reduction serve as one of the most important tasks in China. According to the characteristics of the diversity of power sources, many scholars in China have studied the power carbon footprint and obtained some progress results. In accordance with the current situation of the power industry in Zhejiang Province, based on the net primary productivity model, the IPCC carbon emission method is used to form an applicable power carbon footprint calculation model. The results show that the power carbon footprint of Zhejiang Province increased from 8.399×106 hm2 to 9.659×106 hm2 and then reduced to 5.821×106 hm2 , while the carbon footprint of natural gas power has increased year by year, from 0.022×106 hm2 to 0.185×106 hm2 , and the carbon footprint intensity of power decreased from 17.75×10-4hm2 ·10000 yuan-1 to 9.01 ×10-4hm2 ·10000 yuan-1, indicating that the diversity of power structure in Zhejiang Province is increasing day by day. This paper provides a theoretical basis for energy structure optimization and has a certain application prospect and policy reference value.
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