Multiscale and Real-Time Load Forecasting: A Universal Online Functional Data Analysis Framework
提出Universal-OFA框架，利用函数数据分析和在线深度学习，实现从个体用户到区域的多尺度实时负荷预测，显著降低预测误差成本（个体68.08%，区域80.36%）。
Accurate short-term load forecasting is increasingly required across heterogeneous operating conditions, ranging from individual customers to higher aggregation levels (e.g., districts or regions). However, many existing approaches are developed for a specific setting and scale poorly across aggregation levels, while practical deployment is further complicated by limited historical data (e.g., cold-start users) and the need to adapt as demand patterns evolve. This paper proposes Universal Online Functional Analysis (Universal-OFA), a unified framework for multi-scale, real-time daily load forecasting. The framework is designed to operate consistently across different user types and aggregation levels without full retraining. Universal-OFA represents daily load points as functional curves and organizes them into universal load profiles via a functional clustering module. It then performs real-time forecasting in an online forecasting module with a functional deep neural network that supports lightweight online updates. Using real-world smart meter data, we evaluate Universal-OFA at individual level with existing and new participants, and at higher aggregation levels with varying shares of new participants. Across both levels, Universal-OFA achieves strong forecasting performance, with particularly large improvements in scenarios with more new users. Beyond accuracy, Universal-OFA provides operational value in two ways. First, It supports the monitoring of load usage behavior shifts. Second, cost analysis under asymmetric penalties shows that Universal-OFA significantly decreases the forecast error cost (68.08% at the individual level and 80.36% at higher aggregation levels), indicating clear economic benefits in grid management.