一种基于TCN-ECANet-GRU混合模型的光伏发电短期预测方法
Xiuli Xiang1, Xingyu Li2, Yaoli Zhang2
1School of Economics, Wuhan Textile University, Wuhan, 430077, China.
Scientific reports
|March 21, 2024
概括
这项研究引入了一种新的混合模型,TCN-ECANet-GRU,用于准确的短期光伏 (PV) 功率预测. 与传统方法相比,先进的模型显著提高了预测准确度,确保了更稳定的电源供应.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 时间序列预测时间序列预测
背景情况:
- 传统的光伏电力预测方法与天气不确定性和数据复杂性作斗争.
- 准确的预测对于电网稳定性和可靠的电力供应至关重要.
- 现有的模型往往缺乏现代能源系统所需的精度.
研究的目的:
- 开发一种高精度的短期光伏 (PV) 功率预测方法.
- 解决传统回归和时间序列模型的局限性.
- 提高可再生能源融入电网的可靠性.
主要方法:
- 一种混合模型,将时间卷积网络 (TCN) 结合起来,用于空间特征提取,并将门式循环单位 (GRU) 结合起来,用于时间信息提取.
- 在TCN中整合一个高效的频道注意网络 (ECANet),以改善特征捕获.
- 利用来自澳大利亚光伏发电厂的生成数据进行模型培训和验证.
主要成果:
- 与基线模型相比,TCN-ECANet-GRU模型在所有季节都表现出卓越的性能.
- 获得了极好的准确性,RMSE为0.0195,MAE为0.0128,R2为99.72%.
- 与非最佳模型相比,显示出显著的改进,最大收益为11.32% (RMSE),8.57% (MAE) 和0.38% (R2).
结论:
- 拟议的TCN-ECANet-GRU模型有效地提高了短期光伏功率预测的准确性.
- 该方法对于多步预测 (3, 6, 9 步) 证明了强大.
- 这一进步有助于实现更稳定,更可靠的可再生能源整合.
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