Stacking Model for Photovoltaic-Power-Generation Prediction

Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the p...

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Vydáno v:Sustainability Ročník 14; číslo 9; s. 5669
Hlavní autoři: Zhang, Hongchao, Zhu, Tengteng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2022
Témata:
Accuracy
Algorithms
Alternative energy sources
Carbon
Electricity distribution
Machine learning
Research methodology
Solar energy
Statistical methods
Sustainability
Terms of sale
Australia
China
ISSN:2071-1050, 2071-1050
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Shrnutí:Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation. As a widely used prediction method, the stacking model has been applied in many fields. However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets. The results show that the prediction accuracy of the stacking model is higher than that of the single ensemble-learning model, and that the prediction accuracy of the Stacking-GBDT model is higher than the other stacking models. The stacking model that is proposed in this research provides a reference for the accurate prediction of photovoltaic power generation.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su14095669