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

Maïna André, Sophie Dabo-Niang, Ted Soubdhan and Hanany Ould-Baba

Energy, 2016, vol. 111, issue C, 599-608

Abstract: 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 predictors 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.

Keywords: Satio-temporal vector autoregressive processes; Global solar radiation; Stations' spatial ordering; Selection of temporal order; Short time forecasting (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:111:y:2016:i:c:p:599-608

DOI: 10.1016/j.energy.2016.06.004

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