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dc.contributor.authorLee, Jian Wei.-
dc.description.abstractThis project was to perform prediction of solar energy and radiation in Singapore. Artificial Neural Network (ANN) was used in this project for prediction purpose. Data obtained from National Weather Study Project (NWSP) were used as data sources. Java Object Oriented Neural Engine (JOONE) was used to run the network designed. In this report, the data used were in between 9am to 10am, all together there were 664 datasets. 564 datasets were used for training and the 100 datasets were used for validating the output of network. The error was evaluated by using the 100 validating datasets and their respective network outputs. The accuracies were evaluated within 5% error. In this project, we used the training datasets to teach the ANN so that the network could give us the desired output. The learning algorithm selected in this project was BackPropagation (BP). BP was a method using the difference between the target output value and the network output value to modify the weights of the network. The changes of weights were accumulated during the network learning process. After the learning process, the network was tested with validating data so as to evaluate the accuracy of the network.en_US
dc.format.extent56 p.en_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Power electronicsen_US
dc.titlePrediction of solar energy and radiation in Singaporeen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChan Chee Keongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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