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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