Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2017-11, Vol.93 (1), p.12024
Main Authors: Cheng, K, Guo, L M, Wang, Y K, Zafar, M T
Format: Article
Language:English
Subjects:
Back propagation networks
Cluster analysis
Clustering
Electric power generation
Forecasting
Historical account
Mathematical models
Model accuracy
Neural networks
Photovoltaic cells
Photovoltaics
Prediction models
Screening
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Summary:In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/93/1/012024