Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions?

Short term Load Forecasting (STLF) plays an important role in traditional and
modern power systems. Most STLF models predominantly exploit temporal
dependencies from historical data to predict future consumption. Nowadays, with
the widespread deployment of smart meters, their data can contain
spatiotemporal dependencies. In particular, their consumption data is not only
correlated to historical values but also to the values of neighboring smart
meters. This new characteristic motivates researchers to explore and experiment
with new models that can effectively integrate spatiotemporal interrelations to
increase forecasting performance. Spatiotemporal Graph Neural Networks (STGNNs)
can leverage such interrelations by modeling relationships between smart meters
as a graph and using these relationships as additional features to predict
future energy consumption. While extensively studied in other spatiotemporal
forecasting domains such as traffic, environments, or renewable energy
generation, their application to load forecasting remains relatively
unexplored, particularly in scenarios where the graph structure is not
inherently available. This paper overviews the current literature focusing on
STGNNs with application in STLF. Additionally, from a technical perspective, it
also benchmarks selected STGNN models for STLF at the residential and aggregate
levels. The results indicate that incorporating graph features can improve
forecasting accuracy at the residential level; however, this effect is not
reflected at the aggregate level