基于交互式多尺度卷积模块的综合能源系统的短期多能源消耗预测
Fang Liu1,2, Yucong Huang3, Yalin Wang3,4
1School of Automation, Central South University, Changsha, 410083, Hunan, China. csuliufang@csu.edu.cn.
Scientific reports
|September 13, 2024
概括
本研究引入了一种用于预测综合能源系统 (IES) 能源消耗的新方法. 该方法通过分析多个规模的能源数据和季节性模式来提高预测准确性.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 精确的能源消耗预测对于集成能源系统 (IES) 的稳定和高效运行至关重要.
- 了解消费者能源习惯是优化能源分配和资源管理的关键.
- 现有的预测方法可能无法完全捕捉到能源消耗的复杂相互作用和多尺度特征.
研究的目的:
- 为综合能源系统 (IES) 提出一种先进的短期多元能源消耗预测方法.
- 开发一种新的交互式多尺度卷积模块,用于增强特征提取和融合.
- 通过利用季节性变化和能源间合特征来提高预测性能.
主要方法:
- 开发一个交互式的多尺度卷积模块,用于多尺度的功能融合和交互式学习.
- 实施季节性预测方法,利用不同的季节不同的网络结构.
- 应用基于拉普拉斯分布的损失函数来强大优化联合预测任务.
主要成果:
- 拟议的交互式多尺度卷积模块有效地提取和共享不同尺度的能源消耗之间的合信息,而不增加网络参数.
- 季节性预测方法通过利用季节性和合性特征来证明了增强的预测性能.
- 对比模拟实验验证了拟议方法的有效性和优越性,而不是现有的方法.
结论:
- 开发的方法为IES的短期多元能源消耗预测提供了强大而有效的解决方案.
- 新型卷积模块和季节性适应显著提高了预测准确度.
- 这项研究通过增强的预测能力,有助于综合能源系统的稳定和高效运行.
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