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PV Output forecasting based on weather classification, SVM and ANN

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Title PV Output forecasting based on weather classification, SVM and ANN
 
Creator Agarwal, Varun
Singh, Vatsala
Gaur, Prerna
Agarwal, Rashmi
 
Subject Photovoltaic systems
Solar radiation
Forecasting
Weather classification
Support vector machine
Neural network
 
Description 211-217
The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being setup to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions have affected the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper has proposed a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM (Radial Basis Function Support Vector Machines) into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type has been applied to forecast the solar PV output. The obtained results for the overall model is studied for
its effectiveness and are compared with existing research.
 
Date 2022-05-19T06:42:21Z
2022-05-19T06:42:21Z
2022-04
 
Type Article
 
Identifier 0975-1017 (Online); 0971-4588 (Print)
http://nopr.niscair.res.in/handle/123456789/59745
 
Language en
 
Publisher CSIR-NIScPR, India
 
Source IJEMS Vol.29(2) [April 2022]