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

TL;DR

This paper introduces a hypernetwork-based approach that effectively incorporates external factors like weather and events to improve household-level electrical consumption forecasting accuracy, outperforming existing methods.

Contribution

The study presents a novel hypernetwork architecture that adjusts global models for individual households using external factors, enhancing prediction accuracy in energy consumption forecasting.

Findings

Hypernetworks improve forecasting accuracy with external factors.

The approach reduces prediction errors compared to traditional models.

Global models with hypernetworks outperform existing methods.

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.