Distributed Multi-Head Learning Systems for Power Consumption Prediction

TL;DR

This paper introduces Distributed Multi-Head learning systems tailored for power consumption prediction in smart factories, effectively reducing noise, enhancing accuracy, and preserving data privacy in complex environments.

Contribution

The paper proposes a novel distributed multi-head learning approach that improves prediction accuracy and privacy for power consumption in AGVs within smart factories.

Findings

DMH systems outperform most existing methods in accuracy.

DMH-E reduces prediction error by up to 24%.

Feature engineering and grouping significantly boost performance.

Abstract

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.