Online Model-based Functional Clustering and Functional Deep Learning for Load Forecasting Using Smart Meter Data

Smart meter data analysis is essential for balancing energy consumption and minimizing power outages. However, high-resolution smart meter readings pose challenges to data analysis due to their high volume and dimensions. We propose Online-FDA, an online functional load demand analysis and forecasting framework that incorporates real-time smart meter readings with adaptive clustering to identify daily patterns in functional load consumption and predict daily load demands. This framework utilizes a model-based functional clustering approach assisted by the intra-day load consumption attributes to analyze real-time smart meter data. Moreover, the Online-FDA augments the clusters with a state-of-the-art functional deep neural network that utilizes the training-testing-updating strategy to adaptively learns from real-time smart meter data. Experimental results with real-world smart meter data showed that the proposed Online-FDA is superior to other benchmark algorithms for capturing time-varying variations in load demand, which are essential to the real-time control of electricity grids and the planning of power systems.