  Accurate forecasting of solar power generation with fine temporal and spatial
resolution is vital for the operation of the power grid. However,
state-of-the-art approaches that combine machine learning with numerical
weather predictions (NWP) have coarse resolution. In this paper, we take a
graph signal processing perspective and model multi-site photovoltaic (PV)
production time series as signals on a graph to capture their spatio-temporal
dependencies and achieve higher spatial and temporal resolution forecasts. We
present two novel graph neural network models for deterministic multi-site PV
forecasting dubbed the graph-convolutional long short term memory (GCLSTM) and
the graph-convolutional transformer (GCTrafo) models. These methods rely solely
on production data and exploit the intuition that PV systems provide a dense
network of virtual weather stations. The proposed methods were evaluated in two
data sets for an entire year: 1) production data from 304 real PV systems, and
2) simulated production of 1000 PV systems, both distributed over Switzerland.
The proposed models outperform state-of-the-art multi-site forecasting methods
for prediction horizons of six hours ahead. Furthermore, the proposed models
outperform state-of-the-art single-site methods with NWP as inputs on horizons
up to four hours ahead.
