Short-term Solar Irradiance Forecasting Using Different Artificial Neural Network Algorithms

Volume 4 Issue 2 June - August 2017

Research Paper

Short-term Solar Irradiance Forecasting Using Different Artificial Neural Network Algorithms

Sanjay Kumar Prajapati*, Mukh Raj Yadav**, Kishan Bhushan Sahay***

*Associate Professor, Department of Electrical Engineering, Suyash Institute of Information Technology, Gorakhpur, Uttar Pradesh, India.

**Associate Professor, Department of Electrical Engineering, SUNRISE Institute of Engineering, Technology and Management, Unnao, Uttar Pradesh, India.

***Assistant Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India.

Prajapati, S. K., Yadav, M. R., and Sahay, K. B. (2017). Short-term Solar Irradiance Forecasting Using Different Artificial Neural Network Algorithms. i-manager’s Journal on Pattern Recognition, 4(2), 1-9. https://doi.org/10.26634/jpr.4.2.13723

Abstract

The expectation of sun oriented radiation is essential for a few applications in renewable vitality research. There are various land variables, which influence sun powered radiation forecast, the recognizable proof of these variables for precise sun powered radiation expectation is vital. This paper explores a mixture strategy for the pressure of sun powered radiation utilizing prescient investigation. The forecast of moment insightful sun oriented radiation is performed by utilizing diverse models of Artificial Neural Networks (ANN), to be specific Multi-Layer Perceptron Neural System (MLPNN), Levenberg-Marquardt, Scaled Conjugate Gradient. Root Mean Square Error (RMSE) is utilized to assess the forecast precision of the three ANN models utilized. The data and information picked up from the present study could enhance the precision of examination concerning atmosphere studies and help in blockage control.