| Title |
Research on Data Model Fusion Driven Dual End Uncertainty Source Load Probability Prediction and Its Application in New Energy Power Systems |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.3.389 |
| Keywords |
New energy power system; Two-end uncertainty; Multi-scenario; Source-load probability prediction |
| Abstract |
Despite growing relief 3D printing in arts and manufacturing, issues like inefficient path planning and inadequate accuracy persist. To tackle these problems, a Q-learning algorithm, coupled with an AI evaluation model, is employed to optimize path planning during 3D printing. Within a multi-objective optimization structure, the algorithm is trained to minimize print time, material usage, and enhance accuracy. Relying on a reward mechanism that simulates various path alternatives, the Q-learning algorithm iteratively fine-tunes path selection to strike the optimal cost-efficiency equilibrium. The results showed that after applying the Q-learning algorithm, the path planning efficiency of the relief process was significantly improved. In relief process 1, the optimization time of the Q-learning algorithm was 3.2 hours, with an accuracy of 96.13%. Compared with the 5.4 hours and 90.75% of genetic algorithm and the 6.2 hours and 91.44% of particle swarm algorithm, it showed significant advantages. After 40 iterations, the stability in printing time, material usage, and accuracy metrics indicated that the model reached a stable optimal solution. This research not only enhances relief 3D printing efficiency and accuracy but also presents a universally applicable path optimization approach for other 3D printing technologies, unlocking fresh opportunities for bespoke and customized relief art production. |