Multi-horizon solar radiation forecasting for Mediterranean locations using time series models

Considering the grid manager′s point of view, needs in terms of prediction of intermittent energy like the photovoltaic resource can be distinguished according to the considered horizon: following days (d+1, d+2 and d+3), next day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g....

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Bibliographic Details
Published in:Renewable & sustainable energy reviews 2013-12, Vol.28, p.44-52
Main Authors: Voyant, Cyril, Paoli, Christophe, Muselli, Marc, Nivet, Marie-Laure
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural networks
Autoregressive moving average
Climatology
Computer Science
Energy
Exact sciences and technology
Global radiation
Horizon
Hybrid model
Information Retrieval
Mathematical analysis
Mathematical models
multivariate analysis
Natural energy
Photovoltaic cells
Prediction
Solar cells
Solar energy
Solar radiation
Stationarity
Time series
time series analysis
Weather
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Summary:Considering the grid manager′s point of view, needs in terms of prediction of intermittent energy like the photovoltaic resource can be distinguished according to the considered horizon: following days (d+1, d+2 and d+3), next day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.). Through this work, we have identified methodologies using time series models for the prediction horizon of global radiation and photovoltaic power. What we present here is a comparison of different predictors developed and tested to propose a hierarchy. For horizons d+1 and h+1, without advanced ad hoc time series pre-processing (stationarity) we find it is not easy to differentiate between autoregressive moving average (ARMA) and multilayer perceptron (MLP). However we observed that using exogenous variables improves significantly the results for MLP. We have shown that the MLP were more adapted for horizons h+24 and m+5. In summary, our results are complementary and improve the existing prediction techniques with innovative tools: stationarity, numerical weather prediction combination, MLP and ARMA hybridization, multivariate analysis, time index, etc.
ISSN:1364-0321
1879-0690
DOI:10.1016/j.rser.2013.07.058