以亲和驱动的转移学习为负载预测
Ahmed Rebei1, Manar Amayri1, Nizar Bouguila1
1Concordia Institute for Information Systems Engineering, Montreal, QC H3G1M8, Canada.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了转移学习的任务亲和度评分,提高了负载预测的准确性. 亲和驱动转移学习 (ADTL) 算法增强了对新数据集的预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 能源系统 能源系统
背景情况:
- 准确的负载预测对于高效的能源管理至关重要.
- 传统的转移学习方法在选择合适的源任务时面临挑战.
- 测量任务相似性是预测中有效的知识转移的关键.
研究的目的:
- 引入一种新的任务亲和度评分,用于量化转移学习中的任务相似性.
- 开发用于增强负载预测的亲和驱动转移学习 (ADTL) 算法.
- 为了证明任务亲和度比现有指标的优越性.
主要方法:
- 开发了一个任务亲和度评分来衡量不同任务之间的相似性.
- 提出了以亲密关系驱动的转移学习 (ADTL) 算法,集成预先训练的模型和数据集.
- 使用合成,AEMO和智能澳大利亚能源数据集验证了这一方法.
主要成果:
- 任务亲和度得分在任务选择中表现优于传统指标.
- ADTL算法显著提高了未见数据集的负载预测准确性.
- 经验验证证证实了拟议方法的稳定性和有效性.
结论:
- 任务亲和度评分是完善负载预测中的转移学习的强大工具.
- ADTL算法为准确的能量负载预测提供了一个强大的框架.
- 这项研究促进了转移学习在能源部门的应用.
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