Please use this identifier to cite or link to this item: https://doi.org/10.1109/TENCON.2009.5396082
Title: Neural network-based model for estimation of solar power generating capacity
Authors: Chu, Z.J.
Srinivasan, D. 
Jirutitijaroen, P. 
Issue Date: 2009
Source: Chu, 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. https://doi.org/10.1109/TENCON.2009.5396082
Abstract: Solar 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.
Source Title: IEEE Region 10 Annual International Conference, Proceedings/TENCON
URI: http://scholarbank.nus.edu.sg/handle/10635/71120
ISBN: 9781424445479
DOI: 10.1109/TENCON.2009.5396082
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