多粒度自动变频器用于长期确定性和概率性功率负载预测
Yang Yang1, Yuchao Gao1, Hu Zhou1
1Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
概括
本研究介绍了多粒度自动成型器 (MG-Autoformer),这是长期功率负载预测的高效模型. 它通过捕捉复杂的时间模式和量化预测不确定性,准确地预测未来的电力需求.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 时间序列分析时间序列分析
背景情况:
- 长期电力负载预测对于电力系统规划至关重要.
- 现有的变压器模型面临复杂性和参数开销方面的挑战.
- 负载数据中复杂的时间模式造成了预测困难.
研究的目的:
- 引入一种新的多颗粒度自动成型器 (MG-Autoformer),用于高效的长期负载预测.
- 提高预测未来电力需求的准确性.
- 能够进行概率预测和不确定性量化.
主要方法:
- 开发了一个多颗粒度自动相关注意力机制 (MG-ACAM),以捕捉各种颗粒度的依赖性.
- 实施了共享查询密钥 (Q-K) 机制,以提高效率和降低复杂性.
- 纳入了一个量子损失函数用于概率预测.
主要成果:
- MG-Autoformer在长期负载点预测方面表现出卓越的表现.
- 该模型在概率预测任务中取得了出色的结果.
- 实验证实了各种国际数据集的有效性.
结论:
- 拟议的MG-Autoformer有效地模拟复杂的时间依赖性,以准确的长期负载预测.
- 该模型为点和概率预测提供了一个高效和强大的解决方案.
- MG-Autoformer在功率负载预测方面推进了最先进的技术.
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