一种基于变压器的新型数字双胞胎方法,用于光伏电力预测
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. zhaoamber0629@gmail.com.
Scientific reports
|November 4, 2024
概括
一个新的数字双胞胎模型提高了光伏 (PV) 系统性能预测的准确性高达39.99%. 这种适应性框架为可再生能源发电提供可靠的实时预测.
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
- 可持续的发电是可持续的发电.
背景情况:
- 准确的光伏 (PV) 系统性能预测对于可持续的能源发电和电网集成至关重要.
- 现有的方法经常在PV预测中与域移动和实时适应性作斗争.
- 数字双胞胎 (DT) 技术为能源系统的先进建模和预测提供了潜力.
研究的目的:
- 为增强光伏 (PV) 功率预测提出一个新的数字双胞胎 (DT) 模型.
- 整合域不变特征提取,基于变压器的预测和域适应技术.
- 验证拟议的DT框架对实时和自适应光伏预测的有效性.
主要方法:
- 使用卷积神经网络 (CNN) 来进行域不变特征提取.
- 在核心光伏性能预测任务中使用了变压器模型.
- 集成了一个域适应神经网络 (DANN) 来处理域转移.
- 开发了一个数字双胞胎 (DT) 框架,包含持续的参数更新.
主要成果:
- 与基线模型相比,光伏功率预测的准确性提高了高达39.99%.
- 证明了不同数量的时间的增强性能,表明了适应性.
- 使用光伏功率预测数据集验证了框架的可靠性.
结论:
- 拟议的域匹配数字双胞胎模型显著提高了光伏性能预测的准确性.
- 在DT框架内不断更新参数,增强实时预测能力.
- 这种方法为可再生能源系统中适应性和准确的光伏功率预测提供了可靠的解决方案.
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