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

被引：18

作者：

Do, Minh-Thang ^{[1
]}

Soubdhan, Ted ^{[1
]}

Robyns, Benoit ^{[2
]}

机构：

[1] Univ Antilles Guyane, Lab LARGE, Pointe A Pitre, Guadeloupe, France

[2] Ecole Hautes Etud Ingn HEI, Lab L2EP, Lille, France

来源：

RENEWABLE ENERGY | 2016年 / 85卷

关键词：

PV forecasting models; Neural network; Multivariate model; Forecasting errors; Training duration;

D O I：

10.1016/j.renene.2015.07.057

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：959 / 964

页数：6

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