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From Control to Co-Creation: Predictive Analytics in Resilient Distributed Energy Resources
Linköping University, Department of Management and Engineering, Information Systems and Digitalization. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Management and Engineering, Information Systems and Digitalization. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-3416-4412
Linköping University, Department of Management and Engineering, Information Systems and Digitalization. Linköping University, Faculty of Arts and Sciences.
2025 (English)In: ACIS 2025 Proceedings, AIS , 2025, article id 94Conference paper, Published paper (Other academic)
Abstract [en]

The shift toward decentralised energy generation, driven by the rapid expansion of renewable energy sources, is redefining traditional consumers as active co-creators of electricity generation. This study examines the role of predictive analytics in supporting resilience and co-creation within distributed energy resources (DERs). It employs semi-structured qualitative interviews to explore stakeholder perspectives on how predictive analytics is envisioned and applied. Our preliminary findings show that predictive analytics could be used across diverse applications, such as forecasting, maintenance, and load optimisation. These diverse applications actively shape energy management systems while influencing decision-making and operational design. The application of predictive analytics is shaped by both enabling and constraining conditions. We conclude that predictive analytics functions not only as a technical tool but also aligns stakeholders and promotes collaborative and adaptive energy management among distributed energy resources.

Place, publisher, year, edition, pages
AIS , 2025. article id 94
Keywords [en]
Predictive analytics, co-creation, distributed energy resources, energy management, smart grid
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:liu:diva-221151OAI: oai:DiVA.org:liu-221151DiVA, id: diva2:2036810
Conference
Australasian Conference on Information Systems, 2025
Available from: 2026-02-09 Created: 2026-02-09 Last updated: 2026-05-12
In thesis
1. Predictive Analytics in Smart Grids: Examining the Interplay Between Expectations and Thoughts on Adoption
Open this publication in new window or tab >>Predictive Analytics in Smart Grids: Examining the Interplay Between Expectations and Thoughts on Adoption
2026 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The transition toward smart and more resilient energy systems has become increasingly urgent as electricity grids confront climate change, rapid industrialization, and rising demand. This transition has seen a significant increase in the integration of renewable energy and an increase in the complexity of managing the grid. Thus, predictive analytics is widely promoted as a digital innovation to address this complexity by generating rich insights,providing real-time visibility, and enabling monitoring in smart grids. Yet its adoption remains shaped by stakeholders’ thoughts on its role and value in smart grid operations. This thesis examines the interplay between stakeholder expectations and thoughts on the adoption of predictive analytics in smart grids,focusing on how such expectations are constructed, aligned, and enacted across organizational and institutional contexts. Drawing on Organizing Vision Theory as its primary theoretical lens, the study conceptualizes predictive analytics not only as a technical capability but as a socially embedded innovation whose adoption depends on shared interpretations, legitimacy, and coordinated action. This thesis investigates how policymakers, grid operators,market actors, and energy users interpret the value and role of predictive analytics in the smart grid, and how their expectations of what predictive analytics is shapes thoughts on adoption. Based on an embedded single-case study conducted within the Swedish smart grid context, the findings show that predictive analytics often diffuses through compelling visions that align managerial aspirations, vendor narratives, and policy priorities, amid divergent expectations and institutional logics. By foregrounding expectations as a central analytical object, this thesis contributes to research on digital innovation in complex socio-technical systems and offers insights into how predictive analytics can be adopted and operationalized in the smart grid. At the same time, it highlights how unresolved tensions among stakeholder expectations continue to shape thoughts on adoption, underscoring the importance of collective sensemaking and institutional alignment in the evolution of smart grids.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2026. p. 94
Series
Faculty of Arts and Sciences thesis, ISSN 1401-4637 ; 140Dissertation from the Swedish Research School of Management and Information Technology (MIT). Licentiate theses, ISSN 1653-2554 ; 60
Keywords
Smart grid, predictive analytics, Artificial intelligence, Expectations, Adoption, Organizing Vision
National Category
Information Systems, Social aspects Energy Systems
Identifiers
urn:nbn:se:liu:diva-223869 (URN)10.3384/9789181186215 (DOI)9789181186208 (ISBN)9789181186215 (ISBN)
Presentation
2026-06-10, ACAS, A-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2026-05-12 Created: 2026-05-12 Last updated: 2026-05-19Bibliographically approved

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Kindong, TheodoreJohansson, BjörnPaulsson, Victoria

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