Hybrid methodology for hourly global radiation forecasting in Mediterranean area

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Movin...

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Bibliographic Details
Published in:Renewable energy 2013-05, Vol.53, p.1-11
Main Authors: Voyant, Cyril, Muselli, Marc, Paoli, Christophe, Nivet, Marie-Laure
Format: Article
Language:English
Subjects:
Applied sciences
Autumn
Clouds
Computer Science
Energy
Environmental Engineering
Environmental Sciences
Exact sciences and technology
Forecasting
Information Retrieval
Learning theory
Mathematical models
methodology
Natural energy
Neural networks
Position (location)
prediction
Renewable energy
renewable energy sources
solar radiation
spring
summer
time series analysis
winter
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Summary:The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location. In the best configuration, the coupling of ANN and ARMA allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.
ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2012.10.049