280 research outputs found
Validation Methods for Energy Time Series Scenarios From Deep Generative Models
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.Comment: 20 pages, 8 figures, 2 table
Validation of machine learning based scenario generators
Machine learning methods are getting more and more important in the
development of internal models using scenario generation. As internal models
under Solvency 2 have to be validated, an important question is in which
aspects the validation of these data-driven models differs from a classical
theory-based model. On the specific example of market risk, we discuss the
necessity of two additional validation tasks: one to check the dependencies
between the risk factors used and one to detect the unwanted memorizing effect.
The first one is necessary because in this new method, the dependencies are not
derived from a financial-mathematical theory. The latter one arises when the
machine learning model only repeats empirical data instead of generating new
scenarios. These measures are then applied for an machine learning based
economic scenario generator. It is shown that those measures lead to reasonable
results in this context and are able to be used for validation as well as for
model optimization
Applications of Probabilistic Forecasting in Smart Grids : A Review
This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
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