基于CEEMDAN-TCN-ESN模型的短期功率负载预测
Jiacheng Huang1, Xiaowen Zhang1, Xuchu Jiang1,2
1Zhongnan University of Economics and Law, Wuhan, China.
PloS one
|October 26, 2023
概括
准确的功率负载预测对于高效的能源管理至关重要. 一个新的混合CEEMDAN-TCN-ESN模型显著提高了短期负载预测的准确性,超过了现有的方法.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 高效的电力系统运行依赖于准确的负载预测,以平衡供需.
- 不准确的预测导致能源浪费和经济损失.
- 现有的模型在与功率负载数据的复杂,动态特征作斗争.
研究的目的:
- 开发一种新的混合模型,以提高短期功率负载预测.
- 提高电力负载预测的准确性和效率.
- 为应对复杂负载数据特征带来的挑战.
主要方法:
- 提出了一种混合模型,将完整合体实证模式分解与适应噪声 (CEEMDAN),时间卷积网络 (TCN) 和回声状态网络 (ESN) 结合起来.
- 使用CEEMDAN将负载数据分解为代表不同频率的内在模式函数 (IMF).
- 重建了高频和低频组件,并应用了TCN和ESN进行预测.
主要成果:
- CEEMDAN-TCN-ESN模型在巴拿马的国家电力负载数据上取得了高准确性.
- 实现了 15.081 的根平均平方误差 (RMSE) 和 10.944 的平均绝对误差 (MAE),其 R 平方 (R2) 为 0.994.
- 与CEEMDAN-TCN模型相比,RMSE减少了9.52%,MAE减少了17.39%.
结论:
- 混合CEEMDAN-TCN-ESN模型有效地捕捉了短期功率负载数据中的复杂特征.
- 该模型成功地将基于相似特征的子系列合并在一起,从高频和低频组件中学习.
- 这种方法显示了在短期功率负载预测中实际应用的巨大潜力.
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