Please use this identifier to cite or link to this item: https://doi.org/10.1109/TENCON.2009.5396082
DC FieldValue
dc.titleNeural network-based model for estimation of solar power generating capacity
dc.contributor.authorChu, Z.J.
dc.contributor.authorSrinivasan, D.
dc.contributor.authorJirutitijaroen, P.
dc.date.accessioned2014-06-19T03:20:04Z
dc.date.available2014-06-19T03:20:04Z
dc.date.issued2009
dc.identifier.citationChu, Z.J.,Srinivasan, D.,Jirutitijaroen, P. (2009). Neural network-based model for estimation of solar power generating capacity. IEEE Region 10 Annual International Conference, Proceedings/TENCON : -. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/TENCON.2009.5396082" target="_blank">https://doi.org/10.1109/TENCON.2009.5396082</a>
dc.identifier.isbn9781424445479
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71120
dc.description.abstractSolar energy is one of the most promising renewable energy sources. The generating capacity of this source however is highly dependent on the available sunlight, its duration and intensity. In order to integrate these types of sources into an existing power distribution system, system planners need an accurate model that predicts its generating capacity with the usage of easily accessible information. In this paper, three methods are used to estimate global irradiation received on a tilted surface; mathematical model, regression models and neural network analysis. From the results obtained, the regression model provides the most superior performance. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TENCON.2009.5396082
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TENCON.2009.5396082
dc.description.sourcetitleIEEE Region 10 Annual International Conference, Proceedings/TENCON
dc.description.page-
dc.description.coden85QXA
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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