Three years ahead solar irradiance forecasting to quantify degradation influenced energy potentials from thin film (a-Si) photovoltaic system

•Performance prediction of thin film (a-Si) PV system using machine learning.•Three years ahead energy potentials from a-Si PV modules.•Degradation of a-Si PV system.•Random decision forest machine learning for solar irradiance forecasting. Random forests estimates (RFE) based machine learning algor...

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Bibliographic Details
Published inResults in physics Vol. 12; pp. 701 - 703
Main Authors Manoj Kumar, Nallapaneni, Subathra, M.S.P.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2019
Elsevier
Subjects
Degradation rate
Machine learning
Performance ratio
Solar forecasting
Thin film (a-Si) PV
Year ahead PV power
Solar forecasting
Year ahead PV power
Thin film (a-Si) PV
Degradation rate
Performance ratio
Machine learning
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Summary:•Performance prediction of thin film (a-Si) PV system using machine learning.•Three years ahead energy potentials from a-Si PV modules.•Degradation of a-Si PV system.•Random decision forest machine learning for solar irradiance forecasting. Random forests estimates (RFE) based machine learning algorithm is proposed for forecasting the three years ahead solar irradiance. Accordingly, the corresponding degradation rate (DR) influenced energy potentials are evaluated. Here, the DR of amorphous silicon (a-Si) PV system is estimated based on the simulated performance ratios with historical weather data. Prediction of energy potentials is helpful in decision making on improving the solar power project implementations in selected tropical savanna climates region of south India.
ISSN:2211-3797
2211-3797
DOI:10.1016/j.rinp.2018.12.027