电力消费需求预测的加权全球聚合的联合双边图注意力网络
Ming Yang1, Jianyu Ren1, Lukun Zeng2
1Yunnan Power Grid Co., Ltd, China Southern Power Grid, Kunming, 650000, China.
Scientific reports
|December 21, 2025
概括
本研究介绍了FapDGN,这是一种用于准确预测电力需求的新型联合学习方法. 它有效地模拟时间模式和峰值变化,同时保持数据隐私,优于现有方法.
科学领域:
- 智能电网技术 智能电网技术
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的电力需求预测对于智能电网的稳定性和防止故障至关重要.
- 联合学习 (FL) 为电力消耗数据中的数据稀缺性提供了保护隐私的解决方案.
- 现有的FL方法面临着因参数不一致而导致的超拟合峰值需求变化和过度平滑预测的挑战.
研究的目的:
- 提出一个新的联合学习框架,FapDGN,用于增强电力需求预测.
- 解决现有的FL方法的局限性,特别是需求预测中的过拟合和过平滑.
- 开发一种保护隐私的方法,准确地模拟时间模式和电力使用的峰值变化.
主要方法:
- 提出了一个带有加权全球聚合 (FapDGN) 的联合双边图注意网络.
- 该框架使用图形中的时间和数值结构边缘构建混合特征表示.
- 一个多尺度的注意力机制捕捉时间趋势,而动态协差模型则捕捉峰值变化以减轻过度拟合.
- 基于相似性的自适应动态融合机制用于服务器级参数聚合,以打击过度平滑.
主要成果:
- FapDGN有效地模拟了时间动态和电力消耗的峰值变化.
- 双边图形结构和高斯分布建模减轻了与峰值需求相关的过拟合风险.
- 适应性融合机制成功地减少了全球模型中的过度平滑问题.
- 实验结果表明,FapDGN在预测电力需求方面比传统的FL方法具有更高的性能.
结论:
- 在智能电网中,FapDGN提供了一种强大且保护隐私的解决方案,用于准确预测智能电网的电力需求.
- 提出的方法有效地平衡了时间模式和峰值变化的建模,克服了现有的FL技术的关键局限性.
- FapDGN显示了提高智能电网的运营效率和稳定性的巨大潜力.
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