一个新的混合型TCN-TE-ANN模型,用于高精度的太阳辐射预测
1Computer Engineering, Kirsehir Ahi Evran University, Kirsehir, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种使用时间卷积网络 (TCN),变压器编码器 (TE) 和人工神经网络 (ANN) 进行精确太阳辐射预测的混合模型,改进可再生能源预测.
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
- 太阳能发电预测 太阳能发电预测
背景情况:
- 准确的太阳辐射预测对于优化太阳能系统和电网稳定性至关重要.
- 现有的太阳辐射预测方法在不同位置和分辨率的准确性方面面临挑战.
- 需要取得进展,以提高太阳能集成到电网中的可靠性.
研究的目的:
- 开发和评估一种新的混合模型,用于高精度的全球水平辐射 (GHI) 预测.
- 结合时间卷积网络 (TCN),变压器编码器 (TE) 和人工神经网络 (ANN) 的优势,以改善预测.
- 用美国主要太阳能站点的综合数据集来验证模型的性能.
主要方法:
- 一种混合模型,集成时间卷积网络 (TCN) 来进行时间特征提取.
- 整合变压器编码器 (TE) 以利用注意力机制进行复杂的模式识别.
- 利用人工神经网络 (ANN) 进行最终预测改进,结合TCN和TE.
- 从三个美国太阳能站点获得的30分钟分辨率GHI数据的22年数据集的培训和验证.
主要成果:
- 混合型 TCN-TE-ANN 模型在 GHI 预测中实现了高精度.
- 性能指标包括从0.94768到0.97417.7的R平方值.
- 该模型显示出较低的误差率,RMSE在0.04776和0.06543W/m2之间,MAE在0.02510和0.03526W/m2之间.
- 拟议的模型显著优于现有的太阳辐射预测方法.
结论:
- 新的混合TCN-TE-ANN模型在太阳辐射预测准确度方面取得了重大进展.
- 该模型的架构有效地结合了时间,注意力和密集层处理,以获得卓越的性能.
- 这种方法有望提高太阳能系统的运营效率和电网集成.
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