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An Explainable By-Design Framework for Transparent User-Centric AI Energy Services

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Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops (AIAI 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 715))

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Abstract

This paper explores Explainable Artificial Intelligence (XAI) in the energy sector, highlighting its importance for enhancing transparency, user adoption and engagement. It introduces a User-Centric Explainable By-Design Framework for AI services, aimed at overcoming the “black-box” challenges of AI models by prioritizing explainability and user interaction throughout the AI development process. The framework addresses the deployment of AI in applications such as predictive maintenance, anomaly detection, and photovoltaic (PV) forecasting, emphasizing the need for clear, understandable AI decisions to foster user trust and promote sustainable energy practices. By advocating for a systematic incorporation of XAI principles from data collection to model training and user feedback, the framework seeks to enhance the effectiveness and user experience of AI in the energy sector, supporting a transition towards more efficient and environmentally friendly energy systems. In general, the proposed framework provides a structured approach to achieving these goals, while future research and development in this area should focus on refining and testing the framework across various applications of the energy sector, from smart grids and renewable energy systems to energy efficiency services in residential and commercial buildings.

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Acknowledgement

This work is supported by the DATA CELLAR project, funded by Horizon Europe under Grant Agreement No. 101069694. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or granting authority. Neither the European Union nor the granting authority can be held responsible for them.

Author information

Authors and Affiliations

  1. Trilateral Research, Belview Port, Waterford, Ireland

    Christos Tzouvaras

  2. Department of Cultural Technology and Communication, Intelligent Systems Lab, University of the Aegean (UAEGEAN), Mytilene, Greece

    Christos Tzouvaras, Asimina Dimara & Christos-Nikolaos Anagnostopoulos

  3. Management Science and Technology Department, Democritus University of Thrace (DUTh), Kavala, Greece

    Asimina Dimara & Stelios Krinidis

Authors
  1. Christos Tzouvaras
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  2. Asimina Dimara
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  3. Christos-Nikolaos Anagnostopoulos
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  4. Stelios Krinidis
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Corresponding author

Correspondence to Christos Tzouvaras .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Ionian University, Corfu, Greece

    Ioannis Karydis

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. Hellenic Telecommunications Organization, Athens, Greece

    Ioannis Chochliouros

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Tzouvaras, C., Dimara, A., Anagnostopoulos, CN., Krinidis, S. (2024). An Explainable By-Design Framework for Transparent User-Centric AI Energy Services. In: Maglogiannis, I., Iliadis, L., Karydis, I., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-63227-3_26

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  • DOI: https://doi.org/10.1007/978-3-031-63227-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-63226-6

  • Online ISBN: 978-3-031-63227-3

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