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

2025-01-26

AI Summary

Probabilistic forecasting of electricity consumption across heterogeneous entities—such as households, feeders, and wind turbines—in power systems remains challenging due to poor scalability and limited generalization of conventional entity-wise modeling approaches. Method: This paper proposes GUIDE-VAE, the first unified conditional variational autoencoder framework for multi-entity probabilistic forecasting. It explicitly captures entity heterogeneity via learnable entity embeddings and models temporal dependencies through a novel covariance synthesis module, enabling flexible output—from point estimates to full predictive distributions. Contribution/Results: Evaluated on real-world household load data, GUIDE-VAE reduces CRPS and PINAW by over 12% compared to quantile regression. A single trained model supports real-time inference across thousands of entities, achieving high accuracy, strong cross-entity generalization, and practical deployability in industrial settings.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Electricity Consumption Prediction
• Multi-location Modeling
• Efficiency and Accuracy

Innovation

• GUIDE-VAE
• Unified_Prediction_Model
• High_Precision_and_Scalability