Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/44914
Title: Assessment and prediction of daily average solar radiation In Chonburi with Neural Network Model
Authors: Saithanu, Kidakan
Mekparyup, Jatupat
Keywords: Solar Radiation;Cluster Analysis;Neural Network Model
Issue Date: Sep-2018
Publisher: NISCAIR-CSIR, India
Abstract: Measurements of versatile variables; maximum temperature, minimum temperature, sunshine duration, sea-level pressure, relative humidity, pressure and solar radiation, at Chonburi during 2005 to 2009 were used to assess and build the neural network model for predicting daily average solar radiation. The study results revealed solar radiation and average solar radiation were highest in summer (mid February to mid May) successively in rainy season (mid May to mid October) and winter (mid October to mid February). The days of solar radiation potential were formed into 3 groups by cluster analysis. Cluster 1 represented roughly 6 hours for sunshine duration, 35°C for maximum temperature, 1006 Pa for sea-level pressure and below 70% for relative humidity. Cluster 2 illustrated sunshine duration fluctuated 7 to 11 hours, maximum temperature ranged from 34°C to 37°C, 1007 Pa for sea-level pressure and approximately 70% for relative humidity. Cluster 3 expressed roughly 10 hours for sunshine duration, maximum temperature proximately 33°C, 1008 Pa for sea-level pressure and above 70% for relative humidity.
Page(s): 1834-1837
URI: http://nopr.niscair.res.in/handle/123456789/44914
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.47(09) [September 2018]

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