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Forecasting of photovoltaic power using deep belief network

Lookup NU author(s): Dr Wai Lok Woo, Dr Thillainathan Logenthiran


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© 2017 IEEE. 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.

Publication metadata

Author(s): Neo YQ, Teo TT, Woo WL, Logenthiran T, Sharma A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE Region 10 Annual International Conference, Proceedings/TENCON

Year of Conference: 2017

Pages: 1189-1194

Online publication date: 21/12/2017

Acceptance date: 05/11/2017

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/TENCON.2017.8228038

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509011339