arXiv cs.AI· Huajian Zhang, Mingyue Cheng, Xingpeng Gao, Xiaoyu Tao, Zhiding Liu, Jiqian Yang, Zhi Li·· 11 小时前AI 评分26
ElecLoad：由多事件引导的自适应电力负荷预测智能体
ElecLoad: Adaptive Electricity Load Forecasting Guided by Multiple Events
AI 导读
ElecLoad 是一个面向多事件电力负荷预测的上下文感知智能体，能结合历史负荷与上下文分析各事件对目标负荷的相关性、影响时机及事件间相互作用，据此自适应选择直接预测整个时段或按事件影响变化分段预测。分段时由子智能体独立生成各段初始预测，主智能体审核后子智能体按其反馈做选择性修正。在多个电力负荷数据集上，ElecLoad 在多数评估场景中取得 SOTA 表现，代码已开源。
正文 · 原文
Abstract:Accurate electricity load forecasting provides critical decision support for energy procurement, generation scheduling, and supply-demand balancing, contributing to the economic efficiency and operational reliability of power systems. However, future electricity demand can be affected by the complex interplay of multiple events described in Web notices, weather alerts, and event schedules. Their effects vary over time and can reinforce or offset one another, making electricity load difficult to forecast accurately. While existing methods can incorporate context as additional input, how to use the effects of multiple events and their interactions to guide the load forecasting process itself remains to be explored. To address this gap, we propose ElecLoad, a context-aware agent for multi-event electricity load forecasting that adaptively organizes the forecasting process. Specifically, we train ElecLoad to combine historical load with context to analyze each event's relevance to the target load, the timing of its effects, and potential interactions among events. Guided by this analysis, ElecLoad adaptively chooses to forecast the entire forecast horizon directly or to partition it into segments according to changes in event effects. When the horizon is partitioned, sub-agents independently generate initial forecasts for each segment. The main agent then reviews these forecasts, and the sub-agents perform selective refinement guided by its feedback. Experiments on multiple electricity load datasets show that ElecLoad achieves state-of-the-art performance in most evaluation scenarios. Code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.06508 [cs.AI] |
| (or arXiv:2611.06508v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06508 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Huajian Zhang [view email]
[v1]
Wed, 4 Nov 2026 13:38:13 UTC (1,280 KB)
来源：arXiv cs.AI · arxiv.org