Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks
This addresses energy management efficiency for utilities or grid operators, but it is incremental as it builds on existing hypernetwork methods for a specific domain.
The paper tackled the problem of improving global electrical consumption forecasting by incorporating external factors like weather, which typically degrade performance, and found that using a hypernetwork architecture to adjust model weights per consumer enhanced accuracy, reducing forecasting errors on a dataset of over 6000 households.
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.