一种新的光伏功率预测方法,使用TCN-Wpsformer模型考虑数据修复和FCM集群
Tong Yang1,2, Minan Tang3, Hanting Li1
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China.
Scientific reports
|April 6, 2025
概括
一个新的TCN-Wpsformer模型提高了前一天光伏功率预测的准确性. 这种方法结合了数据恢复和模糊的C-means集群,以改进太阳能预测和电网管理.
科学领域:
- 可再生能源系统可再生能源系统
- 电网中的人工智能
- 机器学习用于预测.
背景情况:
- 准确的短期光伏电力预测对于高效的电力系统调度至关重要.
- 现有的模型往往在太阳能计算成本和预测准确性方面扎.
- 整合不同的数据源和先进的算法是改善预测的关键.
研究的目的:
- 开发一个先进的光伏功率预测模型,以提高准确性和效率.
- 引入一个新的TCN-Wpsformer模型,包括数据恢复和模糊的C-means集群.
- 在准确性和计算成本方面,对现有方法进行模型性能评估.
主要方法:
- 提出了一个TCN-Wpsformer模型,将数据恢复和模糊C-means (FCM) 集群结合起来.
- 在数据恢复和FCM集群后,将时间代码与位置代码拼接在一起.
- 利用时间卷积神经网络 (TCN) 来提取特征,并使用窗口概率稀疏变压器进行多步预测.
主要成果:
- 与标准变压器相比,TCN-Wpsformer模型显示出更好的预测准确性和更低的计算成本.
- 与变压器模型相比,实现了R平方值5.3%的改进,并将计算时间减少了68.83%.
- 该模型在各种数据量和发电站数据集中始终达到99%以上的R平方值.
结论:
- TCN-Wpsformer模型为光伏功率预测提供了卓越的稳定性和跨场景概括能力.
- 该模型与点预测一起提供准确的置信区间,增强其实际应用价值.
- 这种方法在太阳能预测电网集成领域取得了重大进展.
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