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An approach towards demand response optimization at the edge in smart energy systems using local clouds
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-9118-5861
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-3993-3102
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-3874-9968
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-6158-3543
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2023 (English)In: Smart Energy, ISSN 2666-9552, Vol. 12, article id 100123Article in journal (Refereed) Published
Abstract [en]

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 12, article id 100123
Keywords [en]
Demand response optimization, Smart energy systems, AI at the edge, Local cloud-based architecture, Eclipse arrowhead framework, Industry 4.0, Industry 5.0
National Category
Energy Systems Computer Sciences
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-96933DOI: 10.1016/j.segy.2023.100123ISI: 001111778900001Scopus ID: 2-s2.0-85176249058OAI: oai:DiVA.org:ltu-96933DiVA, id: diva2:1753044
Funder
European Commission, 101111977
Note

Validerad;2023;Nivå 2;2023-11-22 (hanlid);

Funder: Arrowhead flexible Production Value Network (fPVN) (101111977); AI-REDGIO5.0; 

Full text license: CC BY-NC-ND

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2024-12-09Bibliographically approved
In thesis
1. Optimizing Smart Industries: Strategies for Efficient System of Systems Development
Open this publication in new window or tab >>Optimizing Smart Industries: Strategies for Efficient System of Systems Development
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The era of extensive digitalization marked by the fourth industrial revolution has ushered in significant advancements in technologies like automation, artificial intelligence, and the Internet of Things (IoT). These innovations are revolutionizing manufacturing processes. Industry 4.0 (I4.0) and the subsequent Industry 5.0 (I5.0) emerged as comprehensive representations of the physical world in the information world, with goals to establish smart factories and promote human-machine coexistence. However, the implementation of I4.0 and I5.0 applications faces challenges related to engineering effort, interoperability, and efficient service discovery and binding.

This thesis seeks to address these challenges by exploring potential strategies to develop an efficient System of Systems (SoS) that comprises individual, autonomous systems collaborating to achieve a shared goal. This research examines methods to enhance the efficacy of SoS by refining its engineering procedures, promoting interoperability between standardized protocols, and employing dynamic adaption mechanisms. It aims to achieve automatic service discovery and interoperability between diverse industrial standards by integrating the Eclipse Arrowhead Framework. This IoT framework facilitates secure and seamless communication and collaboration among devices, machines, and systems.

Moreover, this work delves into saving energy consumption in distributed SoS environments. The thesis aims to optimize energy usage patterns, diminish peak loads, and bolster energy distribution and stability. This is achieved through the Demand Response (DR) mechanism combined with the Eclipse Arrowhead framework. The overarching objective is to pave the way for flexible production processes characterized by minimal resource waste, optimized energy consumption, and sustainable solutions. Through this endeavor, the thesis contributes to shaping a more efficient, interoperable, and sustainable manufacturing landscape in the context of Industry 4.0 and beyond.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2023
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Computer Systems
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-102334 (URN)978-91-8048-430-5 (ISBN)978-91-8048-431-2 (ISBN)
Presentation
2024-01-16, E632, Luleå tekniska universitet, Luleå, 09:30 (English)
Opponent
Supervisors
Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2023-12-11Bibliographically approved
2. Data-Driven Optimizations in Production Value Networks
Open this publication in new window or tab >>Data-Driven Optimizations in Production Value Networks
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industry 4.0 is transforming traditional production systems into dynamic and adaptive value networks. However, this shift reveals significant challenges in achieving seamless interoperability, workflow optimization, and effective value chain analysis within complex production environments. This thesis contributes to addressing these challenges through a structured approach, supporting data-driven and adaptive decision-making to optimize operations in dynamic production value networks.

The research commences by architecting microservice-oriented systems that facilitate the integration of legacy and brownfield technologies with Industry 4.0-compliant environments. By leveraging the Eclipse Arrowhead framework, the thesis demonstrates how diverse systems can exchange data and collaborate at runtime, establishing the foundation for cohesive and interoperable production networks.

Building on this interoperable structure, the thesis explores AI-driven optimizations across key areas, including workflow optimization, predictive maintenance, and demand response. These approaches support operational efficiency and adaptability in production value networks. Case studies showcase collaborative learning models for condition monitoring and an edge-based framework to optimize energy use, demonstrating tangible improvements in efficiency and resilience.

Finally, a significant contribution of this thesis is the introduction of tools for visualization and analysis of value chains. Using the Reference Architectural Model for Industry 4.0 (RAMI 4.0), the research provides methods to map and evaluate value creation within dynamic production networks. By integrating activity-based costing with microservice architectures, it offers granular insights into cost and value dynamics at runtime, enabling agile and informed decision-making in complex industrial environments.

Through these contributions, the thesis advances the understanding and implementation of data-driven optimizations in production value networks, supporting agility and sustainability while contributing to the transition to circular business models by enabling value chain analysis across the product lifecycle. The thesis serves as a bridge between theoretical frameworks and practical applications, providing valuable insights for both academia and industry, and paving the way for more efficient and sustainable production ecosystems.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2025
Keywords
Industry 4.0, Smart Industry Ecosystems, Microservice Architecture, Lifecycle Management, Run-time Value Chain Analysis, Real-time Decision Making, Production Value Networks, Stakeholder Collaboration
National Category
Engineering and Technology
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-111004 (URN)978-91-8048-716-0 (ISBN)978-91-8048-717-7 (ISBN)
Public defence
2025-02-26, A117, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Projects
Arrowhead fPVNAI REDGIO 5.0
Funder
European Commission
Available from: 2024-12-09 Created: 2024-12-09 Last updated: 2025-01-30Bibliographically approved

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Javed, SalmanTripathy, Aparajitavan Deventer, JanMokayed, HamamPaniagua, CristinaDelsing, Jerker

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