In the context of the increasingly severe contradiction between energy supply and demand and the problem of environmental protection, the development and utilization of clean energy have become crucial. In this paper, the optimization problem of power generation is solved by analyzing it at both the macro and micro levels. At the macro level, the relationship between power generation and investment is analyzed by combining machine learning and statistical analysis models, and the consistency of conclusions is verified by building a random forest model. At the microscopic level, photovoltaic (PV) and wind power plants in Xinjiang were used as research subjects, exploring the factors influencing PV and wind power generation through the establishment of a stacked LSTM-LGB-XGB net model. Finally, an optimization strategy based on a linear programming algorithm is proposed, providing an effective method to maximize carbon emission reduction and achieve the optimal combination of investment share, PV power generation, and wind power generation.

Yingjie Sun, Yilin Wang. Optimization of Clean Energy Investment and Power Generation under Carbon Reduction Targets: A Case Study of China. Academic Journal of Business & Management (2024) Vol. 6, Issue 7: 85-95. https://doi.org/10.25236/AJBM.2024.060712.

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