IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2161-d1078234.html
   My bibliography  Save this article

Local Interpretable Explanations of Energy System Designs

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2161/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/5/2161/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Andrea Herbst & Felipe Andrés Toro & Felix Reitze & Eberhard Jochem, 2012. "Introduction to Energy Systems Modelling," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 148(II), pages 111-135, June.
    3. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    4. Howells, Mark & Rogner, Holger & Strachan, Neil & Heaps, Charles & Huntington, Hillard & Kypreos, Socrates & Hughes, Alison & Silveira, Semida & DeCarolis, Joe & Bazillian, Morgan & Roehrl, Alexander, 2011. "OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development," Energy Policy, Elsevier, vol. 39(10), pages 5850-5870, October.
    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.
    7. Weber, Juliane & Heinrichs, Heidi Ursula & Gillessen, Bastian & Schumann, Diana & Hörsch, Jonas & Brown, Tom & Witthaut, Dirk, 2019. "Counter-intuitive behaviour of energy system models under CO2 caps and prices," Energy, Elsevier, vol. 170(C), pages 22-30.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    2. Löffler, Konstantin & Hainsch, Karlo & Burandt, Thorsten & Oei, Pao-Yu & Kemfert, Claudia & Von Hirschhausen, Christian, 2017. "Designing a Model for the Global Energy System—GENeSYS-MOD: An Application of the Open-Source Energy Modeling System (OSeMOSYS)," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(10), pages 1-28.
    3. Sarmiento, Luis & Burandt, Thorsten & Löffler, Konstantin & Oei, Pao-Yu, 2019. "Analyzing Scenarios for the Integration of Renewable Energy Sources in the Mexican Energy System," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(2019), pages 1-1.
    4. Prina, Matteo Giacomo & Manzolini, Giampaolo & Moser, David & Nastasi, Benedetto & Sparber, Wolfram, 2020. "Classification and challenges of bottom-up energy system models - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    5. 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.
    6. Prina, Matteo Giacomo & Casalicchio, Valeria & Kaldemeyer, Cord & Manzolini, Giampaolo & Moser, David & Wanitschke, Alexander & Sparber, Wolfram, 2020. "Multi-objective investment optimization for energy system models in high temporal and spatial resolution," Applied Energy, Elsevier, vol. 264(C).
    7. Luis Sarmiento & Thorsten Burandt & Konstantin Löffler & Pao-Yu Oei, 2019. "Analyzing Scenarios for the Integration of Renewable Energy Sources in the Mexican Energy System—An Application of the Global Energy System Model (GENeSYS-MOD)," Energies, MDPI, vol. 12(17), pages 1-24, August.
    8. Prina, Matteo Giacomo & Lionetti, Matteo & Manzolini, Giampaolo & Sparber, Wolfram & Moser, David, 2019. "Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning," Applied Energy, Elsevier, vol. 235(C), pages 356-368.
    9. Bissiri, M. & Moura, P. & Figueiredo, N.C. & Silva, P.P., 2020. "Towards a renewables-based future for West African States: A review of power systems planning approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    10. Plazas-Niño, F.A. & Ortiz-Pimiento, N.R. & Montes-Páez, E.G., 2022. "National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    11. Konstantin Löffler & Karlo Hainsch & Thorsten Burandt & Pao-Yu Oei & Claudia Kemfert & Christian von Hirschhausen, 2017. "Designing a Global Energy System Based on 100% Renewables for 2050: GENeSYS-MOD: An Application of the Open-Source Energy Modelling System (OSeMOSYS)," Discussion Papers of DIW Berlin 1678, DIW Berlin, German Institute for Economic Research.
    12. Fattahi, A. & Sijm, J. & Faaij, A., 2020. "A systemic approach to analyze integrated energy system modeling tools: A review of national models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    13. Prina, Matteo Giacomo & Groppi, Daniele & Nastasi, Benedetto & Garcia, Davide Astiaso, 2021. "Bottom-up energy system models applied to sustainable islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    14. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    15. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    16. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    17. Dai, Jiangyu & Wu, Shiqiang & Han, Guoyi & Weinberg, Josh & Xie, Xinghua & Wu, Xiufeng & Song, Xingqiang & Jia, Benyou & Xue, Wanyun & Yang, Qianqian, 2018. "Water-energy nexus: A review of methods and tools for macro-assessment," Applied Energy, Elsevier, vol. 210(C), pages 393-408.
    18. Iribarren, Diego & Martín-Gamboa, Mario & Navas-Anguita, Zaira & García-Gusano, Diego & Dufour, Javier, 2020. "Influence of climate change externalities on the sustainability-oriented prioritisation of prospective energy scenarios," Energy, Elsevier, vol. 196(C).
    19. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    20. Navas-Anguita, Zaira & García-Gusano, Diego & Iribarren, Diego, 2019. "A review of techno-economic data for road transportation fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 11-26.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2161-:d:1078234. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.