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

TL;DR

This paper reviews the use of Spatiotemporal Graph Neural Networks in short term load forecasting, highlighting their potential to improve predictions by leveraging spatial dependencies in smart meter data, especially at the residential level.

Contribution

It provides a literature overview and benchmarks STGNN models for load forecasting, demonstrating benefits at residential but not aggregate levels.

Findings

Graph features improve residential load forecasting accuracy.

No significant improvement at aggregate level.

Benchmark results for selected STGNN models.

Abstract

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