在短期电力负载预测中,一个集群分数级灰色模型
Xiang Yu1, Lihua Lu2, Jianming Qi3
1School of Electronics and Information, Shanghai Dianji University, Shuihua Road, Shanghai, 201306, China. yux@sdju.edu.cn.
Scientific reports
|February 20, 2025
概括
一个新的集群分数顺序通用预测控制 (C-FGM) 模型改善了短期电荷预测. 这种数据驱动的方法与现有方法相比,提高了能源需求管理的准确性.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 应用数学 应用数学 应用数学
背景情况:
- 准确的短期电力负载预测对于能源需求管理至关重要.
- 电力消耗数据表现出非静止,非线性和多维的特征,这给预测带来了挑战.
- 分数级偏微分方程在建模复杂的电力消耗行为方面表现有前途.
研究的目的:
- 引入一种新的集群分数顺序通用预测控制 (C-FGM) 模型,用于短期电荷预测.
- 利用分数式微积分和聚类来提高电力系统的预测准确性.
主要方法:
- 开发了C-FGM模型,结合参数α来捕捉集群子系列中的累积天气趋势.
- 利用分数级部分微分方程来建模历史的数序列,在全球范围内优化超参数.
- 在两个真实世界的电力数据集上验证了模型.
主要成果:
- C-FGM模型有效地从数据集中直接学习超参数,以便准确预测.
- 与LSTM (MAPE1.97-4.67%与4.34%) 和变压器 (5.42%) 相比,实现了更高的精度.
- 在平均绝对百分比误差 (MAPE) 中显著改善.
结论:
- C-FGM模型为短期电荷预测提供了强大而准确的解决方案.
- 这种数据驱动的方法为实时能源需求管理提供了有效的工具.
- 分数顺序建模与集群结合,增强了复杂电力系统的预测能力.
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