Photovoltaic (PV) power forecasting plays a crucial role in optimizing the
operation and planning of PV systems, thereby enabling efficient energy
management and grid integration. However, un certainties caused by fluctuating
weather conditions and complex interactions between different variables pose
significant challenges to accurate PV power forecasting. In this study, we
propose PV-Client (Cross-variable Linear Integrated ENhanced Transformer for
Photovoltaic power forecasting) to address these challenges and enhance PV
power forecasting accuracy. PV-Client employs an ENhanced Transformer module to
capture complex interactions of various features in PV systems, and utilizes a
linear module to learn trend information in PV power. Diverging from
conventional time series-based Transformer models that use cross-time Attention
to learn dependencies between different time steps, the Enhanced Transformer
module integrates cross-variable Attention to capture dependencies between PV
power and weather factors. Furthermore, PV-Client streamlines the embedding and
position encoding layers by replacing the Decoder module with a projection
layer. Experimental results on three real-world PV power datasets affirm
PV-Client's state-of-the-art (SOTA) performance in PV power forecasting.
Specifically, PV-Client surpasses the second-best model GRU by 5.3% in MSE
metrics and 0.9% in accuracy metrics at the Jingang Station. Similarly,
PV-Client outperforms the second-best model SVR by 10.1% in MSE metrics and
0.2% in accuracy metrics at the Xinqingnian Station, and PV-Client exhibits
superior performance compared to the second-best model SVR with enhancements of
3.4% in MSE metrics and 0.9% in accuracy metrics at the Hongxing Station.

提出了 PV-Client (Cross-variable Linear Integrated ENhanced Transformer) 用于光伏发电功率预测，采用增强 Transformer 模块捕获不同特征之间的复杂相互作用，使用线性模块学习光伏功率的趋势信息，并整合交叉变量注意力以捕获光伏功率与天气因素之间的依赖关系。实验结果表明，PV-Client 在光伏发电功率预测方面表现卓越。

跨变量线性集成增强型光伏发电功率预测变压器

Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic  power forecasting

Transformer is a state-of-the-art model in the field of natural language
processing (NLP). Current NLP models primarily increase the number of
transformers to improve processing performance. However, this technique
requires a lot of training resources such as computing capacity. In this paper,
a novel structure of Transformer is proposed. It is featured by full layer
normalization, weighted residual connection, positional encoding exploiting
reinforcement learning, and zero masked self-attention. The proposed
Transformer model, which is called Enhanced Transformer, is validated by the
bilingual evaluation understudy (BLEU) score obtained with the Multi30k
translation dataset. As a result, the Enhanced Transformer achieves 202.96%
higher BLEU score as compared to the original transformer with the translation
dataset.

本文提出了一种新型 Transformer 结构，其特点是全层归一化、加权残差连接、利用强化学习的位置编码和零屏蔽自注意力。通过使用 Multi30k 翻译数据集进行双语评估替代，验证了所提出的增强型 Transformer 模型。结果显示，增强型 Transformer 的 BLEU 分数比原始 Transformer 模型高出了 202.96%。