Technical Papers Parallel Session-IV: Photovoltaic power prediction by cascade forward artificial neural network
Abstract/Description
Rapid development of Photovoltaic (PV) power and its efficiency is resulting in significant power supply, global warming mitigation and economic benefits. However the power of PV station is highly affected by weather conditions. Therefore forecast of PV power is essential for planning and operation and management of power system. This paper describes the training, validation and application of cascade feed forward back propagation artificial neural network to predict PV power of 3 KW station installed at North China Electric Power University in Beijing. The input parameters for cascade feed forward neural network are meteorological parameters and the target parameter is real PV power to train the proposed network. Solar radiation, Temperature, Humidity, Wind speed are chosen as input parameters to train the model. Photovoltaic power for two days ahead is predicted with a well-trained cascade feed forward neural network. The results show that the proposed network can precisely compute and forecast the PV power of the test data with accuracy.
Location
Theatre 1, Aman Tower
Session Theme
Technical Papers Parallel Session-IV: Artificial Intelligence
Session Type
Parallel Technical Session
Session Chair
Dr. Syeda Saleha Raza
Start Date
31-12-2017 3:00 PM
End Date
31-12-2017 3:20 PM
Recommended Citation
Khan, I., Zhu, H., Khan, D., & Panjwani, M. K. (2017). Technical Papers Parallel Session-IV: Photovoltaic power prediction by cascade forward artificial neural network. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2017/2017/25
COinS
Technical Papers Parallel Session-IV: Photovoltaic power prediction by cascade forward artificial neural network
Theatre 1, Aman Tower
Rapid development of Photovoltaic (PV) power and its efficiency is resulting in significant power supply, global warming mitigation and economic benefits. However the power of PV station is highly affected by weather conditions. Therefore forecast of PV power is essential for planning and operation and management of power system. This paper describes the training, validation and application of cascade feed forward back propagation artificial neural network to predict PV power of 3 KW station installed at North China Electric Power University in Beijing. The input parameters for cascade feed forward neural network are meteorological parameters and the target parameter is real PV power to train the proposed network. Solar radiation, Temperature, Humidity, Wind speed are chosen as input parameters to train the model. Photovoltaic power for two days ahead is predicted with a well-trained cascade feed forward neural network. The results show that the proposed network can precisely compute and forecast the PV power of the test data with accuracy.
