Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

IF 2.9 3区 工程技术 Q2 AUTOMATION & CONTROL SYSTEMS
Hendro Wicaksono, Martin Trat, Atit Bashyal, Tina Boroukhian, Mine Felder, Mischa Ahrens, Janek Bender, Sebastian Groß, Daniel Steiner, Christoph July, Christoph Dorus, Thorsten Zoerner
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Abstract

The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

Abstract Image

人工智能动态需求响应系统,在生产过程中最大限度地利用可再生能源电力
向可再生能源电力的过渡为制造企业提供了通过参与需求响应计划来节约电力成本的机会。由于多个利益相关者和子系统会产生大量异构数据,因此以制造业电力用户为重点的需求响应系统的端到端实施仍具有挑战性。这项工作为需求响应系统开发了一种利用人工智能的方法,可根据时间可变电价优化工业用户和消费者的生产相关电力成本。它还提出了一种语义中间件架构,利用本体作为语义集成模型来处理系统模块之间的异构数据模型。本文报告了多种用于发电预测和负荷预测的机器学习模型的开发和评估情况,以及混合整数线性规划和强化学习在考虑以绿色电力指数(GEI)为代表的动态电价的情况下用于生产优化的情况。实验表明,混合自动回归长短期记忆模型在太阳能发电预测中表现最佳,卷积神经网络在风力发电预测中表现最佳。随机森林、k-近邻、山脊和梯度提升回归模型在所考虑的使用案例中的负荷预测中表现最佳。此外,本研究还发现,基于强化学习的方法可以为复杂多变的生产环境提供通用且可扩展的解决方案。此外,本文还介绍了所开发系统在德国工业环境中的验证情况,涉及一家公用事业公司和两家中小型制造公司。结果表明,所开发的系统因其灵活的重新安排能力,使实施细粒度流程调度的制造公司受益最大。
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来源期刊
CiteScore
5.70
自引率
17.60%
发文量
2008
审稿时长
62 days
期刊介绍: The International Journal of Advanced Manufacturing Technology bridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration.
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