Industrial energy forecasting using dynamic attention neural networks

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nicholas Majeske , Shreyas Sunil Vaidya , Ryan Roy , Abdul Rehman , Hamed Sohrabpoor , Tyson Miller , Wenhui Li , C.R. Fiddyment , Alexander Gumennik , Raj Acharya , Vikram Jadhao , Prateek Sharma , Ariful Azad
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Abstract

We develop a comprehensive framework for storing, analyzing, forecasting, and visualizing industrial energy systems consisting of multiple devices and sensors. Our framework models complex energy systems as a dynamic knowledge graph, utilizes a novel machine learning (ML) model for energy forecasting, and visualizes continuous predictions through an interactive dashboard. At the core of this framework is A-RNN, a simple yet efficient model that uses dynamic attention mechanisms for automated feature selection. We validate the model using datasets from two manufacturers and one university testbed containing hundreds of sensors. Our results show that A-RNN forecasts energy usage within 5% of observed values. These enhanced predictions are as much as 50% more accurate than those produced by standard RNN models that rely on individual features and devices. Additionally, A-RNN identifies key features that impact forecasting accuracy, providing interpretability for model forecasts. Our analytics platform is computationally and memory efficient, making it suitable for deployment on edge devices and in manufacturing plants.

Abstract Image

基于动态注意力神经网络的工业能源预测
我们开发了一个全面的框架,用于存储、分析、预测和可视化由多个设备和传感器组成的工业能源系统。我们的框架将复杂的能源系统建模为动态知识图,利用新颖的机器学习(ML)模型进行能源预测,并通过交互式仪表板可视化连续预测。该框架的核心是a - rnn,这是一种简单而高效的模型,它使用动态注意力机制进行自动特征选择。我们使用来自两个制造商和一个包含数百个传感器的大学试验台的数据集来验证模型。我们的结果表明,A-RNN预测的能源使用在观测值的5%以内。这些增强的预测比依赖于单个特征和设备的标准RNN模型产生的预测准确率高出50%。此外,A-RNN识别影响预测准确性的关键特征,为模型预测提供可解释性。我们的分析平台具有计算和内存效率,适合部署在边缘设备和制造工厂上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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