针对智能工厂AGV能耗预测，提出分布式多头学习系统。
Distributed Multi-Head Learning Systems for Power Consumption Prediction
- 采用多头机制降低噪声干扰，提升预测精度。
- 在多个数据集上误差比现有系统低14.5%至24.0%。
- 适合关注隐私保护与高效能耗预测的工业场景。
随着自动化车辆在智能工厂中的广泛应用，能耗预测成为任务调度与能源管理的关键问题。尽管交通领域研究较多，但针对智能工厂中自动地面车辆（AGVs）的研究仍较匮乏，其面临复杂环境且产生海量数据。特征多样性与干扰之间存在不可避免的权衡。本文提出分布式多头学习（DMH）系统用于智能工厂中的能耗预测。通过多头学习机制减少噪声干扰并提升准确率。同时，DMH系统采用分布式与分割学习设计，显著降低客户端到服务器的数据传输成本，实现知识共享而无需共享本地数据与模型，增强隐私与安全性。实验结果表明，所提DMH系统在多数数据集和场景下位居前二；其中DMH-E系统相较现有最优方法误差降低14.5%至24.0%。有效性分析验证了基于皮尔逊相关性的特征工程有效性，且结合所提出的多头学习机制进一步提升预测性能。
原文摘要 · Abstract (English)
As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in transportation, but few focus on automatic ground vehicles (AGVs) in smart factories, which face complex environments and generate large amounts of data. There is an inevitable trade-off between feature diversity and interference. In this paper, we propose Distributed Multi-Head learning (DMH) systems for power consumption prediction in smart factories. Multi-head learning mechanisms are proposed in DMH to reduce noise interference and improve accuracy. Additionally, DMH systems are designed as distributed and split learning, reducing the client-to-server transmission cost, sharing knowledge without sharing local data and models, and enhancing the privacy and security levels. Experimental results show that the proposed DMH systems rank in the top-2 on most datasets and scenarios. DMH-E system reduces the error of the state-of-the-art systems by 14.5% to 24.0%. Effectiveness studies demonstrate the effectiveness of Pearson correlation-based feature engineering, and feature grouping with the proposed multi-head learning further enhances prediction performance.
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