Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks

2025-06-17

AI Summary

To address the degradation of global model performance in household-level electricity consumption forecasting when incorporating external factors (e.g., weather, holidays), this paper proposes a hypernetwork-based dynamic weight adaptation method. It is the first to introduce hypernetworks into load forecasting, enabling a single global model to generate personalized parameters for each household based on its unique features—thereby overcoming the limitation of conventional fixed-parameter models in capturing user heterogeneity. The approach integrates multi-source time-series data and embeds external factors via learned encodings. Evaluated on a high-resolution two-year dataset comprising over 6,000 households in Luxembourg, the method achieves significant reductions in MAE and RMSE, outperforming state-of-the-art global models while maintaining efficient training, strong generalization, and low computational overhead—offering a lightweight, deployable solution for smart grid applications.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Accurate electrical consumption forecasting is crucial for efficient energy management and resource allocation. While traditional time series forecasting relies on historical patterns and temporal dependencies, incorporating external factors -- such as weather indicators -- has shown significant potential for improving prediction accuracy in complex real-world applications. However, the inclusion of these additional features often degrades the performance of global predictive models trained on entire populations, despite improving individual household-level models. To address this challenge, we found that a hypernetwork architecture can effectively leverage external factors to enhance the accuracy of global electrical consumption forecasting models, by specifically adjusting the model weights to each consumer. We collected a comprehensive dataset spanning two years, comprising consumption data from over 6000 luxembourgish households and corresponding external factors such as weather indicators, holidays, and major local events. By comparing various forecasting models, we demonstrate that a hypernetwork approach outperforms existing methods when associated to external factors, reducing forecasting errors and achieving the best accuracy while maintaining the benefits of a global model.

Problem

• Improving household-level electrical consumption forecasting accuracy
• Incorporating external factors without degrading global model performance
• Enhancing global models using hypernetworks for individual adjustments

Innovation

• Hypernetwork architecture adjusts weights per consumer
• Incorporates weather, holidays, and local events
• Outperforms traditional global forecasting models