Study on the calculation of new energy subsidies based on multiple linear regression

Chen Tan; Jing Wu; Hao Fan; Hao Xu; Mingyu Hua; Shouqin Wang

doi:10.1117/12.2647070

7 September 2022 Study on the calculation of new energy subsidies based on multiple linear regression

Chen Tan, Jing Wu, Hao Fan, Hao Xu, Mingyu Hua, Shouqin Wang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291E (2022) https://doi.org/10.1117/12.2647070
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

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.

Citation Download Citation

Chen Tan, Jing Wu, Hao Fan, Hao Xu, Mingyu Hua, and Shouqin Wang "Study on the calculation of new energy subsidies based on multiple linear regression", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291E (7 September 2022); https://doi.org/10.1117/12.2647070

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