# An Unsupervised Deep Learning Approach for Scenario Forecasts

arXiv: https://arxiv.org/abs/1711.02247 · HTML: https://mlsanity.com/paper/1711.02247/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Principal Component Density Estimation for Scenario Generation Using Normalizing Flows](https://mlsanity.com/paper/2104.10410/) (2021)
- [Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06147/) (2022)
- [Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts](https://mlsanity.com/paper/2207.09818/) (2022)
- [ScenGAN: Attention-Intensive Generative Model for Uncertainty-Aware Renewable Scenario Forecasting](https://mlsanity.com/paper/2509.17119/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-driven, producing a set of scenarios that represent possible future behaviors based only on historical observations and point forecasts. It first applies a newly-developed unsupervised deep learning framework, the generative adversarial networks, to learn the intrinsic patterns in historical renewable generation data. Then by solving an optimization problem, we are able to quickly generate large number of realistic future scenarios. The proposed method has been applied to a wind power generation and forecasting dataset from national renewable energy laboratory. Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations. Our code and simulation datasets are freely available online.