  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.
