In the context of increasing demands for long-term multi-energy load
forecasting in real-world applications, this paper introduces Patchformer, a
novel model that integrates patch embedding with encoder-decoder
Transformer-based architectures. To address the limitation in existing
Transformer-based models, which struggle with intricate temporal patterns in
long-term forecasting, Patchformer employs patch embedding, which predicts
multivariate time-series data by separating it into multiple univariate data
and segmenting each of them into multiple patches. This method effectively
enhances the model's ability to capture local and global semantic dependencies.
The numerical analysis shows that the Patchformer obtains overall better
prediction accuracy in both multivariate and univariate long-term forecasting
on the novel Multi-Energy dataset and other benchmark datasets. In addition,
the positive effect of the interdependence among energy-related products on the
performance of long-term time-series forecasting across Patchformer and other
compared models is discovered, and the superiority of the Patchformer against
other models is also demonstrated, which presents a significant advancement in
handling the interdependence and complexities of long-term multi-energy
forecasting. Lastly, Patchformer is illustrated as the only model that follows
the positive correlation between model performance and the length of the past
sequence, which states its ability to capture long-range past local semantic
information.

Patchformer 是一种整合了补丁嵌入 (patch embedding) 和编码 - 解码式 Transformer 架构的新型模型，用于解决现有基于 Transformer 的模型在长期预测中难以应对复杂时间模式的问题，通过将多元时间序列数据分解为多个单元数据并将其分割为多个补丁，Patchformer 有效地提高了模型捕捉局部和全局语义依赖性的能力，数值分析表明，在新颖的多能源数据集和其他基准数据集上，Patchformer 在多元和单元长期预测中均能够获得更好的准确性，此外，研究还发现了能源相关产品之间相互依赖对 Patchformer 和其他对比模型长期时间序列预测性能的积极影响，以及 Patchformer 相对于其他模型的优越性，这在处理长期多能源预测的相互依赖性和复杂性方面具有重大的进展，最后，Patchformer 作为唯一一个模型跟踪过去序列长度与模型性能之间的正相关关系，突出了其捕捉长程过去局部语义信息的能力。