Enriched global horizontal irradiance prediction using novel ensemble improved backpropagation neural network

Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India – 247667, Orcid ID: 0000-0003-2552-0400

^* Corresponding author email: mmadhiarasan.cse@sric.iitr.ac.in

Abstract

Penetration of solar energy into the power grid and smart grid is becoming an urge because of the continuous progress in industrialization and advancement. Requires a high accurate Global Horizontal Irradiance (GHI) prediction to achieve effective penetration of solar energy. This paper proposes a novel Ensemble Improved Backpropagation Neural Network (EIBPNN) with enhanced generalization ability because it is developed based on the various inputs’ individual improved backpropagation neural networks. Hence, the variance of individual IBPNN and input parameters based uncertainty are overcome and has the generic performance capability. The comparative analysis imparts the proposed prediction model results improved GHI prediction than the existing models. The proposed model has enriched GHI prediction with better generalization.