An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya
The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation i...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-06-01.
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| Online Access: | Get fulltext |
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| LEADER | 02211 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23419_15026 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Kurniawan, Adi |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Harumwidiah, Anisa |e author |
| 245 | 0 | 0 | |a An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23419 | ||
| 520 | |a The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Renewable energy | ||
| 690 | |a machine learning; mean average percentage error; mean squared error; renewable energy; weather parameter; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 3: June 2021; 1245-1250 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23419/15026 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23419/15026 |z Get fulltext |