Estimation of Solar Radiation Using Machine Learning Techniques (Record no. 14703)

MARC details
000 -LEADER
fixed length control field 02859nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.47
Item number C495E
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Chaturvedi, Aniket
245 ## - TITLE STATEMENT
Title Estimation of Solar Radiation Using Machine Learning Techniques
Statement of responsibility, etc by Aniket Chaturvedi
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Electrical Engineering
Year of publication 2018
300 ## - PHYSICAL DESCRIPTION
Number of Pages xv,43p.
Other physical details HB
520 ## - SUMMARY, ETC.
Summary, etc Solar irradiance is an ultimate source of solar energy and it depends on various parameters including temperature, humidity, wind speed etc.Therefore, prediction of solar irradiance will help in planning and designing efficient solar energy system. Many research groups have attempted to predict the solar irradiance using other geographical parameter with the help of machine learning algorithms. Out of them artificial neural networks comes out to be the most common. In the present research work a variety of neural network architectures are implemented to find out the best and optimum method to predict the solar irradiance.In addition, all the other parameters such as training function (trainlm, trainbr, and trainscg).Present research work shows an implementation of artificial neural network, recurrent neural network, general regression neural network and radial basis function network fully crossed with three training functions (trainbr, trainscg, and trainlm). The output is judged on three other metrics apart from the correlation between original and predicted solar irradiance. Thesemetrics consists of mean bias error(MBE),mean absolute error (MAE), root mean square error (RMSE). All the methods are implemented on dataset obtained from national solar radiation database (NSRDB). The dataset contain eight other attributes apart from solar irradiance for eight years (2000-2007). The prediction of global solar irradiance is done by taking all other eight attributes as input. Proposedmethods are also implemented on different ratios of training and testing data ranging from 50% to 90% in training and 50% to 10 % in testing. Further, implementation of principal componentanalysis is also done to reduce the redundancy between attributes.Overall, in all the methods general regression neuralnetworkcomes out to be the most efficient one by producing99.44% correlation coefficient between predicted and original global solar irradiance. This showsbetter performanceof general regression neural network overcommon artificial neural network. The error terms are also minimumin the case of general regression neural network.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Estimation of Solar Radiation
Topical Term MTech Theses
Topical Term Department of Electrical Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Yadav, Sandeep Kumar
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
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      Not For Loan Reference S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Course Reserve 2024-01-24 621.47 C495E TM00123 2024-01-24 Thesis