Abstract :
[en] 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.
