Optimizing Attention in a Transformer for Multihorizon, Multienergy Load Forecasting in Integrated Energy Systems

变压器 调度(生产过程) 计算机科学 二次方程 嵌入 符号 电气工程 工程类 数学优化 电压 人工智能 数学 算术 几何学
作者
Jili Fan,Wei Zhuang,Min Xia,Wenxuan Fang,Jun Liu
出处
期刊:IEEE Transactions on Industrial Informatics [Institute of Electrical and Electronics Engineers]
卷期号:20 (8): 10238-10248 被引量:1
标识
DOI:10.1109/tii.2024.3392278
摘要

Accurate forecasting of multienergy loads is essential for designing, operating, scheduling, and managing integrated energy systems (IESs). Recent research suggests that transformer models have the potential to improve long-sequence predictions. However, existing transformer models often emphasize capturing temporal dependencies while neglecting crucial dependencies among different variables necessary for multienergy load forecasting. Moreover, transformer models encounter challenges related to quadratic time complexity and significant memory usage, which hinder their direct applicability to tasks involving long-sequence, multienergy load forecasting. To tackle these challenges, we propose a model called DTformer and apply it to the task of multihorizon, multienergy load forecasting in IES. Within DTformer, we employ patch embedding to convert the input multienergy load sequences into a 3-D vector array, preserving both temporal and variable information. Subsequently, we propose the temporal top windowed attention (TWA) module and the dual variable attention module to handle extended temporal dependencies and intervariable dependencies. Importantly, the computational complexity and memory requirements of the TWA model are regulated at a level of $O(N^\frac{4}{3})$ . Through extensive experimentation, we found that our DTformer surpasses baseline models in terms of performance using the IES dataset sourced from Arizona State University's Tempe campus.
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