A study on the minimum duration of training data to provide a high accuracy forecast for PV generation between two different climatic zones | Soubdhan, Ted
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A study on the minimum duration of training data to provide a high accuracy forecast for PV generation between two different climatic zones

Archive ouverte : Article de revue

Soubdhan, Ted | Do, Minh-Thang | Robyns, Benoît

Edité par HAL CCSD ; Elsevier

International audience. This study focus on the minimum duration of training data required for PV generation forecast. In order to investigate this issue, the study is implemented on 2 PV installations: the first one in Guadeloupe represented for tropical climate, the second in Lille represented for temperate climate; using 3 different forecast models: the Scaled Persistence Model, the Artificial Neural Network and the Multivariate Polynomial Model. The usual statistical forecasting error indicators: NMBE, NMAE and NRMSE are computed in order to compare the accuracy of forecasts. The results show that with the temperate climate such as Lille, a longer training duration is needed. However, once the model is trained, the performance is better.

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