Validation Methods for Energy Time Series Scenarios From Deep Generative Models

IEEE Access, vol. 10, pp. 8194–8207

Abstract

The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electricity prices. These are typically represented by a set of discrete realizations known as scenarios. A popular scenario generation approach uses deep generative models (DGM) that allow scenario generation without prior assumptions about the data distribution. However, the validation of generated scenarios is difficult, and a comprehensive discussion about appropriate validation methods is currently lacking. To start this discussion, we provide a critical assessment of the currently used validation methods in the energy scenario generation literature. In particular, we assess validation methods based on probability density, auto-correlation, and power spectral density. Furthermore, we propose using the multifractal detrended fluctuation analysis (MFDFA) as an additional validation method for non-trivial features like peaks, bursts, and plateaus. As representative examples, we train generative adversarial networks (GANs), Wasserstein GANs (WGANs), and variational autoencoders (VAEs) on two renewable power generation time series (photovoltaic and wind from Germany in 2013 to 2015) and an intra-day electricity price time series form the European Energy Exchange in 2017 to 2019. We apply the four validation methods to both the historical and the generated data and discuss the interpretation of validation results as well as common mistakes, pitfalls, and limitations of the validation methods. Our assessment shows that no single method sufficiently characterizes a scenario but ideally validation should include multiple methods and be interpreted carefully in the context of scenarios over short time periods.

Authors 6

1. Eike Cramer Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research—Energy Systems Engineering (IEK-10), Jülich, Germany

   RWTH Aachen University, Aachen, Germany

2. Leonardo Rydin Gorjão

   Forschungszentrum Jülich · University of Cologne · OsloMet – Oslo Metropolitan University · Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

   Affiliation as printed

   Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway

   Forschungszentrum Jülich, Institute for Energy and Climate Research—Systems Analysis and Technology Evaluation (IEK-STE), Jülich, Germany

   German Aerospace Center (DLR), Institute of Networked Energy Systems, Oldenburg, Germany

   Institute for Theoretical Physics, University of Cologne, Köln, Germany

3. Alexander Mitsos Aachen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research—Energy Systems Engineering (IEK-10), Jülich, Germany

   JARA-ENERGY, Jülich, Germany

   RWTH Aachen University—Process Systems Engineering (AVT.SVT), Aachen, Germany

4. Benjamin Schäfer

   Queen Mary University of London · Norwegian University of Life Sciences

   Affiliation as printed

   Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway

   School of Mathematical Sciences, Queen Mary University of London, London, U.K

5. Dirk Witthaut

   Forschungszentrum Jülich · University of Cologne

   Affiliation as printed

   Forschungszentrum Jülich, Institute for Energy and Climate Research—Systems Analysis and Technology Evaluation (IEK-STE), Jülich, Germany

   Institute for Theoretical Physics, University of Cologne, Köln, Germany

6. Manuel Dahmen

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research—Energy Systems Engineering (IEK-10), Jülich, Germany