ITM Web Conf.
Volume 45, 20222021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|Number of page(s)||8|
|Section||Computer Technology and System Design|
|Published online||19 May 2022|
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: firstname.lastname@example.org
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.
Key words: Ensemble / Improved backpropagation neural network / Global horizontal irradiance / and prediction
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.