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Local Interpretable Explanations of Energy System Designs

Author

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  • Jonas Hülsmann

    (Energy Information Networks & Systems, Technical University Darmstadt, 64283 Darmstadt, Germany)

  • Julia Barbosa

    (Energy Information Networks & Systems, Technical University Darmstadt, 64283 Darmstadt, Germany)

  • Florian Steinke

    (Energy Information Networks & Systems, Technical University Darmstadt, 64283 Darmstadt, Germany)

Abstract

Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct sensitivity analysis is not easy for human decision-makers, since they may become overloaded by the multitude of possible results. We thus propose transferring an approach from explaining complex neural networks, so-called locally interpretable model-agnostic explanations (LIME), to this related problem. Specifically, we use variations of a small number of interpretable, high-level parameter features and sparse linear regression to obtain the most important local explanations for a selected design quantity. For a small bottom-up optimization model of a grid-connected building with photovoltaics, we derive intuitive explanations for the optimal battery capacity in terms of different cloud characteristics. For a larger application, namely a national model of the German energy transition until 2050, we relate path dependencies of the electrification of the heating and transport sector to the correlation measures between renewables and thermal loads. Compared to direct sensitivity analysis, the derived explanations are more compact and robust and thus more interpretable for human decision-makers.

Suggested Citation

  • Jonas Hülsmann & Julia Barbosa & Florian Steinke, 2023. "Local Interpretable Explanations of Energy System Designs," Energies, MDPI, vol. 16(5), pages 1-17, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2161-:d:1078234
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    References listed on IDEAS

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    1. Julia Barbosa & Christopher Ripp & Florian Steinke, 2021. "Accessible Modeling of the German Energy Transition: An Open, Compact, and Validated Model," Energies, MDPI, vol. 14(23), pages 1-18, December.
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    5. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    6. Mohamed Chaibi & EL Mahjoub Benghoulam & Lhoussaine Tarik & Mohamed Berrada & Abdellah El Hmaidi, 2021. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction," Energies, MDPI, vol. 14(21), pages 1-19, November.
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    1. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.

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