基于多变量时间序列预测的注意力模型:多阶段太阳辐射预测
Sadman Sakib1, Mahin K Mahadi1, Samiur R Abir1
1Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, 1704, Bangladesh.
Heliyon
|March 18, 2024
概括
在孟加拉国,准确的太阳辐射预测对于管理太阳能发电至关重要. 基于注意力的模型,特别是时间融合变压器 (TFT),显著提高了光伏系统的预测准确性.
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:
- 孟加拉国亚热带气候提供高太阳能电池板效率.
- 准确的太阳辐射预测对于连接到电网的光伏系统来管理功率变化至关重要.
- 现有的预测模型在与太阳辐射数据固有的长期顺序依赖性作斗争.
研究的目的:
- 开发和评估基于注意力的模型框架,用于多变量太阳辐射时间序列预测.
- 评估基于注意力的编码器-解码器,变压器和时间融合变压器 (TFT) 模型的性能.
- 将这些先进的模型与传统预测方法进行比较.
主要方法:
- 利用了来自孟加拉国两个地点的30分钟分辨率太阳辐射数据.
- 训练并测试了基于注意力的编码器-解码器,变压器和时间融合变压器 (TFT) 模型.
- 评估了用于预测太阳辐射的模型性能,提前24步.
主要成果:
- 注意力机制显著提高了预测的准确性.
- 与其他模型相比,时间融合变压器 (TFT) 显示出卓越的精度和稳定性.
- TFT实现了0.151的平均平方误差 (MSE),0.212的平均绝对误差 (MAE) 和0.815的R平方 (R2).
结论:
- 基于注意力的模型,特别是TFT,对于太阳辐射预测非常有效.
- 与基准和顺序模型相比,TFT提供了显著的改进,将MSE降低了高达47.9%,MAE降低了高达22.3%.
- 注意力模型捕捉长距离依赖性的能力提高了太阳能应用的预测能力.
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