Forecasting day-ahead electricity prices with spatial dependence

Abstract

Market integration connects multiple autarkic electricity markets and facilitates the flow of power across areas. More often than not, market integration can increase social welfare for the whole power system with a clear spatial dependence structure among area electricity prices, which motivates a new perspective on electricity price forecasting. In this paper, we construct a model to forecast the day-ahead electricity prices of Nord Pool with spatial dependence. First of all, we convert the electricity prices into graph data. Then, we propose an STGNN (Spatial-Temporal Graph Neural Network) model to exploit spatial and temporal features. In particular, the STGNN model can accurately forecast electricity prices for multiple areas, where the adjacency matrix representing the spatial dependence structure is pre-captured by the R-vine (regular vine) copula. Our results show that the spatial dependence structure described by the R-vine copula can perfectly reflect the physical characteristics of the electricity system; moreover, the forecasting performance of the proposed STGNN model is significantly better than the existing models in terms of overall accuracy and hourly accuracy within a day.