1,879 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
Denoising diffusion probabilistic models for probabilistic energy forecasting
Scenario-based probabilistic forecasts have become vital for decision-makers
in handling intermittent renewable energies. This paper presents a recent
promising deep learning generative approach called denoising diffusion
probabilistic models. It is a class of latent variable models which have
recently demonstrated impressive results in the computer vision community.
However, to our knowledge, there has yet to be a demonstration that they can
generate high-quality samples of load, PV, or wind power time series, crucial
elements to face the new challenges in power systems applications. Thus, we
propose the first implementation of this model for energy forecasting using the
open data of the Global Energy Forecasting Competition 2014. The results
demonstrate this approach is competitive with other state-of-the-art deep
learning generative models, including generative adversarial networks,
variational autoencoders, and normalizing flows.Comment: Version accepted to Powertech 2023. arXiv admin note: text overlap
with arXiv:2106.09370, arXiv:2107.0103
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