Predictive spatio-temporal model for spatially sparse global solar radiation data

Maïna André (1) , Ted Soubdhan (2) , Hanany Ould-Baba (3) , Sophie Dabo-Niang (4, 5)

1 Université des Antilles (Pôle Guadeloupe)
2 GRER - Groupe de Recherche sur les Energies Renouvelables
3 LMAC - Laboratoire de Mathématiques Appliquées de Compiègne
4 GREMARS - Groupe de Recherches Modélisation Appliquée à la Recherche en Sciences Sociales
5 LEM - Lille économie management - UMR 9221

This paper introduces a new approach for the forecasting of solar radiation series at a located station for very short time scale. We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km. The proposed model is a spatio temporal vector autoregressive VAR model specifically designed for the analysis of spatially sparse spatio-temporal data. This model differs from classic linear models in using spatial and temporal parameters where the available pre-dictors are the lagged values at each station. A spatial structure of stations is defined by the sequential introduction of predictors in the model. Moreover, an iterative strategy in the process of our model will select the necessary stations removing the uninteresting predictors and also selecting the optimal p-order. We studied the performance of this model. The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales. Moreover, we compared the results of our model to simple and well known persistence model and those found in literature.

Mots clés: Stations' spatial ordering; intra-hour forecasting; Selection of temporal order; spatio-temporal vector autoregressiv processs

Domaines: Autre

Maïna André, Ted Soubdhan, Hanany Ould-Baba, Sophie Dabo-Niang. Predictive spatio-temporal model for spatially sparse global solar radiation data. Energy, 2016, 111, pp.599 - 608. ⟨10.1016/j.energy.2016.06.004⟩. ⟨hal-01823261⟩