用Transformer模型提升短时光伏功率预测精度。
SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
- 基于自注意力机制捕捉气象数据中的时空依赖关系。
- 在晴天和阴天均表现优异，鲁棒性强。
- 融入电站元信息，支持跨地域泛化，适合实际部署。
准确预测光伏功率输出对可再生能源并网至关重要。本文提出一种基于注意力机制的深度学习模型SolarTformer，用于短时光伏功率预测。该模型利用Transformer架构，从气象数据中预测光伏输出。与传统模型不同，SolarTformer通过自注意力机制有效捕捉太阳辐照度的时间依赖性和空间变化性。此外，模型引入电站特定元数据，增强不同地理位置、组件配置及季节条件下的泛化能力。实验表明，SolarTformer在相同数据集上显著优于已有模型，尤其在晴天与阴天均表现出色，体现强鲁棒性与泛化能力。研究结果表明，基于注意力的架构能有效提升光伏预测精度，助力可再生能源系统更可靠管理。
原文摘要 · Abstract (English)
Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based deep learning model, inspired by transformer architecture, is used for short-term solar power forecasting. Our proposed model, "SolarTformer", is designed to predict solar power output from meteorological data. Unlike traditional models, SolarTformer leverages self-attention mechanisms to effectively capture temporal dependencies and spatial variability in solar irradiance. In addition, the proposed methodology includes feeding power station-specific metadata into the model, which helps to generalize between power stations located at different locations and with different panel configurations and in different seasons. Our experiments demonstrate that SolarTformer significantly outperforms previous models on the same data set. In particular, the model exhibits strong performance on both clear and cloudy days, indicating high robustness and generalizability. These findings highlight the potential of attention-based architectures in enhancing the accuracy of solar forecasting, contributing to a more reliable management of renewable energy.
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