Local Interpretable Explanations of Energy System Designs

Jonas Hülsmann (), Julia Barbosa and Florian Steinke
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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

Energies, 2023, vol. 16, issue 5, 1-17

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.

Keywords: energy system design models; explainable AI (XAI); LIME; sensitivity analysis; decision makers (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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