Berlin 2024 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 1: Tutorial: Dynamics of Economic and Financial Systems (joint session SOE/TUT)
SOE 1.2: Tutorial
Sunday, March 17, 2024, 16:45–17:30, H 1012
Exploring electricity price dynamics with interpretable machine learning — •Benjamin Schäfer1 and Dirk Witthaut2 — 1Karlsruhe Institute of Technology (KIT) — 2Research Center Jülich (FZJ)
Mitigation of climate change requires a fundamental transformation of our energy system. Power plants based on fossil fuels must be replaced by renewable power sources, such as wind and solar power. This energy transition (Energiewende) towards a sustainable energy system raises numerous complex challenges, as power generation becomes more uncertain, while simultaneously more operational data becomes available. Hence, data-driven approaches have become feasible and even necessary to fully understand the energy systems of today and tomorrow across all scales.
Machine learning and artificial intelligence can handle these enormous amounts of data but need to do so in a transparent way. Obtaining classifications or forecasts without explanations limits their use severely.
Within this tutorial, we will discuss the uses of machine learning in energy systems and review approaches to make initial 'black box' models transparent. As an application, we will consider electricity markets and price dynamics.
Keywords: Price Dynamics; Machine Learning; Interpretations; Power Grids; AI