Estimation of potential solar energy in MTI region (Malaysia, Thailand and Indonesia) based on linear, nonlinear and artificial neural network models
Solar radiation (SR) data offer information on the amount of the sun potential at a location on the earth during a specific time. These data are very important for designing sizing solar photovoltaic (PV) systems. Due to the high cost of installation and fitting troubles, these barriers cause lack...
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| Format: | Thesis |
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| Language: | English |
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77971/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77971/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77971/4/Hudhaifa.pdf |
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| Summary: | Solar radiation (SR) data offer information on the amount of the sun potential at a location on the earth during a specific time. These data are very important for designing sizing solar photovoltaic (PV) systems. Due to the high cost of installation
and fitting troubles, these barriers cause lack of data and make data availability difficult. Prediction models for solar radiation are the key solution to substitute these important data and cover the missing from it. Therefore, there is a demand to develop alternative ways of predicting these data. The zone of Malaysia, Thailand and Indonesia (MTI), which are part of southeast Asia (SEA), is a huge area Had no model can cover all regions but only individual models assigned to particular countries. On
the other hand, the zone (MTI) had practiced many types of modeling techniques for solar radiation prediction, with variation in its prediction attitude and results accuracy; hence, it is very important to implement a comparison between models in
order to find the most accurate one. Best prediction model according to accuracy,
need to be compared with other similar neighbor models within the same zone. This
study presents linear, non-linear models as MTI linear and MTI nonlinear
models in order to develop a standardization modeling technique in this zone and
Artificial neural network (ANN) models has been implemented also in the same
area to predict its global and diffuse solar radiation. The different models have
been tested in different areas. These areas a r e classified as zone, region and
globally. It is found that the zone and region models are accurate and could be
used to predict solar radiation, which is an interested achievement. Nevertheless,
global models have a high error percentage. The results showed that the ANN
models are accurate in comparison with the nonlinear and linear models in which
the mean absolute percentage error (MAPE) in calculating the solar energy in
Malaysia by the ANN model is 5.3%, while the MAPE for the MTI nonlinear and
linear models is 6.4%, 7.3% respectively. In addition, the root mean square error
(RMSE) shows the following promising results, 7.2% for ANN model and 8.1%,
8.5% for the MTI nonlinear and linear models respectively. Finally, the mean bias
error (MBE) comes up with these next results ANN model is -1.3%, the MTI
nonlinear model is -1.1% and MTI linear model is -1.1%. |
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