用超网络融合外部因素，提升千户级用电预测精度。
Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks
- 用超网络动态调整每户的模型权重以响应天气等外部因素
- 在6000+户数据上，误差显著降低，优于现有全局模型
- 适合能源管理、智能电网等需要高精度分户预测的场景
精准的用电量预测对高效能源管理与资源配置至关重要。传统时间序列方法依赖历史模式和时间依赖性，而引入天气、节假日等外部因素可显著提升复杂场景下的预测准确率。然而，这些特征常导致基于全人群训练的全局模型性能下降，尽管个体家庭模型表现提升。为此，我们发现超网络架构能有效利用外部因素，通过为每个用户特定调整模型权重，提升全局预测模型的准确性。我们收集了覆盖两年、来自6000多户卢森堡家庭的用电数据及对应的天气、节假日、重大本地事件等外部信息。通过对比多种模型，验证了结合外部因素的超网络方法在降低预测误差方面优于现有方法，实现最佳精度的同时保留全局模型优势。
原文摘要 · Abstract (English)
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.
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