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

Minh-Thang Do, Ted Soubdhan and Benoît Robyns,

Renewable Energy, 2016, vol. 85, issue C, 959-964

Abstract: 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.

Keywords: PV forecasting models; Neural network; Multivariate model; Forecasting errors; Training duration (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:85:y:2016:i:c:p:959-964

DOI: 10.1016/j.renene.2015.07.057

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