Forecasting of photovoltaic power using deep belief network

Neo, Y. Q., Teo, Tiong Teck, Woo, Wai Lok, Logenthiran, Thillainathan and Sharma, A. (2017) Forecasting of photovoltaic power using deep belief network. In: TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, pp. 1189-1194. ISBN 978-1-5090-1135-3

Full text not available from this repository.

Abstract

This main focus of this paper aims to forecast photovoltaic power. The accuracy for forecasting Renewable Energy Sources (RES) are important as it is needed for power grids to operate. It can help make necessary adjustments to operate with RES, which can be highly complexed. As penetration level of renewable generation increases overtime, there may result in a shift towards a generation-dominant grid, causing severe power quality concerns. The proposed methodology of this paper is artificial neural network (ANN) and the training algorithm is Deep Belief Network (DBN). The parameters that are used to configure the software are studied in close observation. The objective of this paper is to determine the parameters of the DBN to accurately forecast photovoltaic power. The proposed methodology is validated by cross-validation and comparing it with another training algorithm.

Item Type:	Book Section
Uncontrolled Keywords:	Photovoltaic, Forecasting, Deep Learning, Deep Belief Network, Renewable energy resources
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	29 Mar 2019 11:09
Last Modified:	10 Oct 2019 20:49
URI:	http://nrl.northumbria.ac.uk/id/eprint/38630

Downloads