mlsanityData to 7 Oct 2026

Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators

arXiv 2204.02242 ↗First posted 5 Apr 2022math.OC cs.LGcited by 3

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Abstract (arXiv)

We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power scenarios by sampling from a conditional distribution that uses wind speed forecasts to tailor the scenarios to a specific day. We apply the generated scenarios in a stochastic day-ahead bidding problem of a wind electricity producer and analyze whether the scenarios yield profitable decisions. Compared to Gaussian copulas and Wasserstein-generative adversarial networks, the normalizing flow successfully narrows the range of scenarios around the daily trends while maintaining a diverse variety of possible realizations. In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more stable profitable results compared to an unconditional selection of historical scenarios. The normalizing flow consistently obtains the highest profits, even for small sets scenarios.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 72 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

20 of 72 references.

References · 20

  1. 2022Generative Adversarial Networkscited by 5,603
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8 more
  1. 2018Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworkscited by 21
  2. 2021A deep generative model for probabilistic energy forecasting in power systems: normalizing flowscited by 18
  3. 2019Scenario Forecasting of Residential Load Profilescited by 11
  4. 2021Principal Component Density Estimation for Scenario Generation Using Normalizing Flowscited by 10
  5. 2020Changes in electricity demand pattern in Europe due to COVID-19 shutdownscited by 5
  6. 2021Validation Methods for Energy Time Series Scenarios from Deep Generative Modelscited by 5
  7. 2021Ancillary services in Great Britain during the COVID-19 lockdown: a glimpse of the carbon-free futurecited by 2
  8. 2021A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended versioncited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2022Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flowscited by 14
  2. 2023Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flowscited by 2
  3. 2024Least Squares and Marginal Log-Likelihood Model Predictive Control using Normalizing Flowscited by 1