Day-ahead renewable scenario forecasts based on generative adversarial networks

Abstract

<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>

Generative Adversarial Network and Its Application in Energy Internet

“…Scenario generation helps to simulate the uncertainty and variability of renewable energy power generation. It is an important tool for the decisionmaking of renewable energy with high permeability power [48]. GAN is applied to the scene generation of renewable resources and uses the ability of a deep neural network and a large amount of historical data to directly generate scenes consistent with the same historical data distribution.…”

A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

“…Recently, VAEs [30,31] and GANs [12,32,25,33] have been both used to generate PV, wind power, and load scenarios. They both make probabilistic forecasts in the form of Monte Carlo samples that can be used to compute consistent quantile estimates for all sub-ranges in the prediction horizon.…”

“…In contrast, deep generative models such as Variational AutoEncoders (VAEs) [10] and Generative Adversarial Networks (GANs) [11] directly learn a generative process of the data. They have demonstrated their effectiveness in many applications to compute accurate probabilistic forecasts including power system applications [12,13,14,15]. This study investigates the implementation of Normalizing Flows [16,NFs] in power system applications.…”