Advancing Long-Term Multi-Energy Load Forecasting with Patchformer: A Patch and Transformer-Based Approach

arXiv:2404.10458v1 Announce Type: new
Abstract: 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 …

abstract applications architectures arxiv context cs.ai cs.lg decoder embedding encoder encoder-decoder energy forecasting long-term novel paper patterns struggle temporal transformer transformer-based models type world