One Model to Forecast Them All and in Entity Distributions Bind Them

TL;DR

This paper introduces GUIDE-VAE, a single, scalable probabilistic forecasting model using a conditional variational autoencoder, which effectively captures uncertainty and dependencies in multi-entity power system data.

Contribution

The study presents GUIDE-VAE, a novel single-model approach for entity-specific probabilistic forecasting that outperforms traditional methods and offers flexible, interpretable outputs.

Findings

GUIDE-VAE outperforms quantile regression in accuracy

It provides scalable, entity-specific probabilistic forecasts

The model captures uncertainty and temporal dependencies effectively

Abstract

Probabilistic forecasting in power systems often involves multi-entity datasets like households, feeders, and wind turbines, where generating reliable entity-specific forecasts presents significant challenges. Traditional approaches require training individual models for each entity, making them inefficient and hard to scale. This study addresses this problem using GUIDE-VAE, a conditional variational autoencoder that allows entity-specific probabilistic forecasting using a single model. GUIDE-VAE provides flexible outputs, ranging from interpretable point estimates to full probability distributions, thanks to its advanced covariance composition structure. These distributions capture uncertainty and temporal dependencies, offering richer insights than traditional methods. To evaluate our GUIDE-VAE-based forecaster, we use household electricity consumption data as a case study due to its multi-entity and highly stochastic nature. Experimental results demonstrate that GUIDE-VAE outperforms conventional quantile regression techniques across key metrics while ensuring scalability and versatility. These features make GUIDE-VAE a powerful and generalizable tool for probabilistic forecasting tasks, with potential applications beyond household electricity consumption.