An Improved Method for Photovoltaic Forecasting Model Training Based on Similarity

Photovoltaic (PV) power generation is the most widely adopted renewable energy source. However, its inherent unpredictability poses considerable challenges to the management of power grids. To address the arduous and time-consuming training process of PV prediction models, which has been a major foc...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 9; p. 2119
Main Authors Liu, Limei, Chen, Jiafeng, Liu, Xingbao, Yang, Junfeng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 06.05.2023
Subjects
Accuracy
Alternative energy sources
Carbon
Datasets
Deep learning
Dimensional analysis
Electric power generation
Electricity
Energy management systems
Forecasting
Machine learning
Methods
Neural networks
Pandemics
Photovoltaic cells
Photovoltaic power generation
Prediction models
Renewable energy sources
Renewable resources
Seasonal variations
Similarity
Solar energy
Time series
Training
China
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Summary:Photovoltaic (PV) power generation is the most widely adopted renewable energy source. However, its inherent unpredictability poses considerable challenges to the management of power grids. To address the arduous and time-consuming training process of PV prediction models, which has been a major focus of prior research, an improved approach for PV prediction based on neighboring days is proposed in this study. This approach is specifically designed to handle the preprocessing of training datasets by leveraging the results of a similarity analysis of PV power generation. Experimental results demonstrate that this method can significantly reduce the training time of models without sacrificing prediction accuracy, and can be effectively applied in both ensemble and deep learning approaches.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12092119