Title :

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

Author(s) :

André, Maïna [Auteur]
Université des Antilles (Pôle Guadeloupe)
Soubdhan, Ted [Auteur]
Groupe de Recherche sur les Energies Renouvelables [GRER]
Ould-Baba, Hanany [Auteur]
Laboratoire de Mathématiques Appliquées de Compiègne [LMAC]
Dabo-Niang, Sophie [Auteur]
Groupe de Recherches Modélisation Appliquée à la Recherche en Sciences Sociales [GREMARS]
Lille économie management - UMR 9221 [LEM]

Journal title :

Energy

Pages :

599 - 608

Publisher :

Elsevier

Publication date :

2016-09

ISSN :

0360-5442

English keyword(s) :

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

HAL domain(s) :

Planète et Univers [physics]/Autre

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL