Estimation of Solar Radiation Using Machine Learning Techniques (Record no. 14703)
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000 -LEADER | |
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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 |
Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Accession Number | Price effective from | Koha item type |
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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 |