Forecasting of photovoltaic power using deep belief network

Y. Q. Neo, T. T. Teo, W. L. Woo, T. Logenthiran, A. Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
Number of pages6
ISBN (Electronic)9781509011339
ISBN (Print)9781509011353
Publication statusPublished - 21 Dec 2017

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON


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