在混合组合框架中选择动态模型,以进行强大的光伏电力预测
Nakhun Song1, Roberto Chang-Silva1, Kyungil Lee1
1Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea.
这项研究引入了一个灵活的混合组合 (FHE) 框架,用于准确的太阳能预测. 通过动态选择模型,FHE框架提高了预测准确性,优于现有方法.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
背景情况:
- 全球不断增长的电力需求和环境问题推动了可再生能源的采用.
- 太阳能提供了一个具有成本效益和可部署的解决方案,但它的间歇性质给预测带来了挑战.
- 准确的太阳能预测对于电网稳定性和高效的能源管理至关重要.
研究的目的:
- 开发一个新的灵活混合组合 (FHE) 框架,用于增强太阳能预测.
- 使用预测错误模式动态选择最佳基准模型,提高预测准确度.
- 为小型分布式太阳能发电系统提供强大且可扩展的预测解决方案.
主要方法:
- 提出了一个灵活的混合组合 (FHE) 框架,使用元模型进行动态基础模型选择.
- 通过使用四座太阳能发电厂的现实数据评估了FHE框架.
- 与最先进的模型和传统的混合组合技术对比FHE框架.
主要成果:
- FHE框架显示出优异的预测性能,平均绝对百分比错误 (MAPE) 比SVR模型提高了30%.
- 在不同的天气条件下,FHE模型保持了高精度.
- 该框架消除了对基础模型和组合模型的初步验证的需求,简化了部署.
结论:
- 拟议的FHE框架为太阳能发电预测提供了一个强大而可扩展的解决方案.
- 基于错误模式的动态模型选择可以提高预测的准确性和可靠性.
- FHE框架简化了分布式太阳能发电系统的部署过程.
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