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

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. (c) 2015 Elsevier Ltd. All rights reserved.