@inproceedings{d3f9495d1c5145d783a7a0fbdf0a49b8,
title = "Forecasting of photovoltaic power using deep belief network",
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.",
keywords = "Deep Belief Network, Deep Learning, Forecasting, Photovoltaic, Renewable energy resources",
author = "Neo, {Y. Q.} and Teo, {T. T.} and Woo, {W. L.} and T. Logenthiran and A. Sharma",
year = "2017",
month = dec,
day = "21",
doi = "10.1109/TENCON.2017.8228038",
language = "English",
isbn = "9781509011353",
series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",
publisher = "IEEE",
pages = "1189--1194",
booktitle = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",
address = "United States",
}
