# Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems

arXiv: https://arxiv.org/abs/2409.07637 · HTML: https://mlsanity.com/paper/2409.07637/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [A New Framework for Distance and Kernel-based Metrics in High Dimensions](https://mlsanity.com/paper/1909.13469/) (2019)
- [Inferring serial correlation with dynamic backgrounds](https://mlsanity.com/paper/2101.10962/) (2021)
- [GLASSO Model for Electric Load and Wind Power and Monte Carlo Scenario Generation](https://mlsanity.com/paper/2111.14628/) (2021)

## Built on by (most cited first, 9 of 9)

- [Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models](https://mlsanity.com/paper/2510.02224/) (2025)
- [Conformal Predictive Distributions for Order Fulfillment Time Forecasting](https://mlsanity.com/paper/2505.17340/) (2025)
- [Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption](https://mlsanity.com/paper/2411.12193/) (2024)
- [Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction](https://mlsanity.com/paper/2510.15780/) (2025)
- [Decision-calibrated prediction sets for robust power system operations](https://mlsanity.com/paper/2606.02081/) (2026)
- [CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting](https://mlsanity.com/paper/2602.02583/) (2026)
- [The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality](https://mlsanity.com/paper/2510.12271/) (2025)
- [Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction](https://mlsanity.com/paper/2607.17095/) (2026)
- [SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting](https://mlsanity.com/paper/2607.20587/) (2026)

## Abstract (arXiv)

The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the development of new techniques for reliable and efficient forecasting. This paper proposes a method combining probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power in high-dimensional contexts. By incorporating weather covariates and restoring spatio-temporal correlations, the proposed method enhances the reliability of probabilistic forecasts in RES. Extensive numerical experiments compare the effectiveness of different time series models, with performance evaluated using comprehensive metrics on a real-world and high-dimensional dataset from Midcontinent Independent System Operator (MISO). The results highlight the importance of weather information and demonstrate the efficacy of the Gaussian copula in generating realistic scenarios, with the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model showing superior performance.