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An Explainable AI-Based Framework for Supporting Decisions in Energy Management

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Machine Learning Applications for Intelligent Energy Management

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 35))

Abstract

Climate change and energy production and consumption are two inextricably linked concrete concepts of great concern. In an attempt to guarantee our future, the European Union (EU) has prioritized the addressing of both concepts, creating a new social contract between its citizens and the environment. The dazzling progress in its methodologies and applications during the recent years and the familiarization of the public with its abilities indicate Artificial Intelligence (AI) as a potential and powerful tool towards addressing important threats that climate change imposes. However, when using AI as a tool, it is vital to do so responsibly and transparently. Explainable Artificial Intelligence (xAI) has been coined as the term that describes the route of responsibility when implementing AI-driven systems. In this paper, we expand applications that have been previously built to address the problem of energy production and consumption. Specifically, (i) we conduct a survey to key stakeholders of the energy sector in the EU, (ii) we analyse the survey to define the required depth of AI explainability and (iii) we implement the outcomes of our analysis by developing a useful xAI framework that can guarantee higher adoption rates for our AI system and a more responsible and safe space for that system to be deployed.

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Notes

  1. 1.

    In this work, the term “energy producer” refers to an electricity producer, but, in more general terms, the term may as well refer to a natural gas producer.

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Acknowledgements

The work presented is based on research conducted within the framework of the project “Modular Big Data Applications for Holistic Energy Services in Buildings (MATRYCS)”, of the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 101000158 (https://matrycs.eu/), of the Horizon 2020 European Commission project BD4NRG under grant agreement no. 872613 (https://www.bd4nrg.eu/) and of the Horizon Europe European Commission project DigiBUILD under grant agreement no. 101069658 (https://digibuild-project.eu/). The authors wish to thank the Coopérnico team, whose contribution, helpful remarks and fruitful observations were invaluable for the development of this work. The content of the paper is the sole responsibility of its authors and does not necessary reflect the views of the EC.

Author information

Authors and Affiliations

  1. Decision Support Systems Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece

    Elissaios Sarmas, Vangelis Marinakis & Haris Doukas

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Dimitrios P. Panagoulias, George A. Tsihrintzis & Haris Doukas

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  1. Elissaios Sarmas
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  2. Dimitrios P. Panagoulias
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  3. George A. Tsihrintzis
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  4. Vangelis Marinakis
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Correspondence to Elissaios Sarmas .

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Editors and Affiliations

  1. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Haris Doukas

  2. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Vangelis Marinakis

  3. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Elissaios Sarmas

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Sarmas, E., Panagoulias, D.P., Tsihrintzis, G.A., Marinakis, V., Doukas, H. (2024). An Explainable AI-Based Framework for Supporting Decisions in Energy Management. In: Doukas, H., Marinakis, V., Sarmas, E. (eds) Machine Learning Applications for Intelligent Energy Management. Learning and Analytics in Intelligent Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-031-47909-0_1

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