使用交互功能趋势变压器进行中长期光伏功率预测的新型模型
Xiang Liu1, Qingyu Liu1, Shuai Feng2
1College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.
Scientific reports
|February 24, 2025
概括
准确的中长期太阳能预测对于电网稳定性至关重要. 新的IFTformer深度学习模型通过预处理数据和单独建模季节性和趋势组件来提高预测准确性.
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:
- 光伏发电 (PV) 发电本质上是随机的,影响电网稳定性.
- 准确的中长期光伏电力预测仍然是一个重大挑战.
- 现有的方法在较长时间内难以应对光伏输出的变化和复杂性.
研究的目的:
- 开发一个先进的深度学习模型,以改善中长期光伏功率预测.
- 为了提高光伏电力预测的准确性,通用性和稳定性.
- 解决当前预测模型在处理数据异常值和复杂时间模式方面的局限性.
主要方法:
- 设计了一种混合深度学习模型,即交互式特征趋势转换器 (IFTformer).
- 数据预处理涉及使用深度隔离森林 (DIF) 和局部异常因子 (LOF) 删除异常值.
- 时间序列分解分离季节和趋势组件,使用ProSparse自我注意力和多层感知器 (MLP) 建模.
主要成果:
- 与基线模型相比,IFTformer表现出优异的预测性能.
- 实现了3.64%的正常化根平均平方误差 (NRMSE) 和2.44%的正常化平均绝对误差 (NMAE).
- 该模型有效地减轻了异常影响,并增强了特征提取能力.
结论:
- IFTformer为中长期光伏功率预测提供了有效的解决方案.
- 该模型提高了预测准确性,概括性和稳定性.
- 介绍了一种新的深度学习方法,用于中长期的光伏电力预测.
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