International Journal of Mathematics Trends and Technology
Volume 71 | Issue 3 | Year 2025 | Article Id. IJMTT-V71I3P104 | DOI : https://doi.org/10.14445/22315373/IJMTT-V71I3P104
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 10 Jan 2025 | 21 Feb 2025 | 11 Mar 2025 | 30 Mar 2025 |
In the context of globalization, climate change and environmental issues have received increasing attention. For a long time, relying on traditional fossil fuels for electricity production has led to significant greenhouse gas emissions, one of the key factors contributing to global warming. New energy power generation has become important to address global warming and promote sustainable energy development. With the progress of science and technology and the expansion of production scale, the utilization of new energy will improve the conversion efficiency of energy resources and reduce production and conversion costs. This study aims to utilize the methods of Random Forest and Long Short-Term Memory (LSTM) neural networks to research new energy generation power. By collecting and analyzing the historical data of wind power generation and solar power generation, a prediction model based on Random Forest and LSTM neural network was established. The experimental results show that this model can accurately predict the changing trends of new energy generation power. For example, when predicting solar power generation power, the model’s accuracy can reach 96.72 percent and 97.37 percent, effectively reducing the prediction errors. Through in-depth research on new energy generation power, we can better understand the utilization potential of renewable energy, provide decision-making support for power grid dispatching and energy planning, promote the development of sustainable energy, improve the power generation efficiency of the power grid, and ensure the safety and stability of the power grid.
Random Forest, Long Short-Term Memory (LSTM), New Energy, Generation Power Prediction.
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Xinlong Zhang, "Research on New Energy Power Generation based on Random Forest and LSTM," International Journal of Mathematics Trends and Technology (IJMTT), vol. 71, no. 3, pp. 33-53, 2025. Crossref, https://doi.org/10.14445/22315373/IJMTT-V71I3P104
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