Indian Journal of Engineering & Materials Sciences

Open Access Open Access  Restricted Access Subscription Access

PV Output forecasting based on weather classification, SVM and ANN


Affiliations
1 Netaji Subhas Institute of Technology, University of Delhi, Delhi 110 078,, India
2 J C Bose University of Science & Technology, YMCA, Faridabad, Haryana 121 006, India

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.
User
Notifications
Font Size

Abstract Views: 166




  • PV Output forecasting based on weather classification, SVM and ANN

Abstract Views: 166  | 

Authors

Varun Agarwal
Netaji Subhas Institute of Technology, University of Delhi, Delhi 110 078,, India
Vatsala Singh
J C Bose University of Science & Technology, YMCA, Faridabad, Haryana 121 006, India
Prerna Gaur
J C Bose University of Science & Technology, YMCA, Faridabad, Haryana 121 006, India
Rashmi Agarwal
J C Bose University of Science & Technology, YMCA, Faridabad, Haryana 121 006, India

Abstract


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.