Spatiotemporal Behind-the-Meter Load and PV Power Forecasting via Deep Graph Dictionary Learning

Abstract

In recent years, with the rapid growth of rooftop photovoltaic (PV) generation in distribution networks, power system operators call for accurate forecasts of the behind-the-meter (BTM) load and PV generation. However, the existing forecasting methodologies are incapable of quantifying such BTM measurements as the smart meters can merely measure the net load time series. Motivated by this challenge, this article presents the spatiotemporal BTM load and PV forecasting (ST-BTMLPVF) problem. The objective is to disaggregate the historical net loads of neighboring residential units into their BTM load and PV generation and forecast the future values of these unobservable time series. To solve ST-BTMLPVF, we model the units as a spatiotemporal graph (ST-graph) where the nodes represent the net load measurements of units and edges reflect the mutual correlation between the units. An ST-graph autoencoder (ST-GAE) is devised to capture the spatiotemporal manifold of the ST-graph, and a novel spatiotemporal graph dictionary learning (STGDL) optimization is proposed to utilize the latent features of the ST-GAE to find the most significant spatiotemporal features of the net load. STGDL utilizes the captured features to estimate the historical BTM load and PV measurements, which are further used by a deep recurrent structure to forecast the future values of BTM load and PV generation at each unit. Numerical experiments on a real-world load and PV data set show the state-of-the-art performance of the proposed model, both for the BTM disaggregation and forecasting tasks.