A multi-source data power load forecasting method using attention mechanism-based parallel cnn-gru

TL;DR

The document presents a dual-layout LaTeX class system (cas-sc.cls for single-column and cas-dc.cls for two-column) designed to streamline manuscript preparation for Elsevier submissions. It provides a complete template with flexible front matter handling, including long front matter, author/affiliation metadata, and various author-marking options to denote corresponding authors and affiliations. The classes support advanced formatting features such as theorem-like environments and customized list macros, enhancing consistency and readability. By aligning with Elsevier's updated submission workflow, the template reduces formatting overhead and improves interoperability across journals. This work facilitates efficient, standardized preparation of scholarly manuscripts for publication.

Abstract

Accurate power load forecasting is crucial for improving energy efficiency and ensuring power supply quality. Considering the power load forecasting problem involves not only dynamic factors like historical load variations but also static factors such as climate conditions that remain constant over specific periods. From the model-agnostic perspective, this paper proposes a parallel structure network to extract important information from both dynamic and static data. Firstly, based on complexity learning theory, it is demonstrated that models integrated through parallel structures exhibit superior generalization abilities compared to individual base learners. Additionally, the higher the independence between base learners, the stronger the generalization ability of the parallel structure model. This suggests that the structure of machine learning models inherently contains significant information. Building on this theoretical foundation, a parallel convolutional neural network (CNN)-gate recurrent unit (GRU) attention model (PCGA) is employed to address the power load forecasting issue, aiming to effectively integrate the influences of dynamic and static features. The CNN module is responsible for capturing spatial characteristics from static data, while the GRU module captures long-term dependencies in dynamic time series data. The attention layer is designed to focus on key information from the spatial-temporal features extracted by the parallel CNN-GRU. To substantiate the advantages of the parallel structure model in extracting and integrating multi-source information, a series of experiments are conducted.