Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs
This paper presents Artificial Neural Network (ANN) technique for predicting the output power from Grid-Connected Photovoltaic (GCPV) system. Different inputs are utilized in several models of ANN in order to obtain the output power. ANN parameters are chosen using trial-and-error method to find the...
| الحاوية / القاعدة: | Proceedings - 2016 IEEE Conference on Systems, Process and Control, ICSPC 2016 |
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| المؤلف الرئيسي: | |
| التنسيق: | Conference paper |
| اللغة: | English |
| منشور في: |
Institute of Electrical and Electronics Engineers Inc.
2017
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019976825&doi=10.1109%2fSPC.2016.7920728&partnerID=40&md5=81a26452858fff8c13cf93a4afe0696a |
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Nordin N.; Sulaiman S.I.; Omar A.M. |
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Nordin N.; Sulaiman S.I.; Omar A.M. 2-s2.0-85019976825 Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs 2017 Proceedings - 2016 IEEE Conference on Systems, Process and Control, ICSPC 2016 10.1109/SPC.2016.7920728 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019976825&doi=10.1109%2fSPC.2016.7920728&partnerID=40&md5=81a26452858fff8c13cf93a4afe0696a This paper presents Artificial Neural Network (ANN) technique for predicting the output power from Grid-Connected Photovoltaic (GCPV) system. Different inputs are utilized in several models of ANN in order to obtain the output power. ANN parameters are chosen using trial-and-error method to find the optimal value of root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R2). The study involved training and testing mode through ANN. Result showed that ANN model 1 (with 6 input data) performs superior result compared to the other models. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
| author |
2-s2.0-85019976825 |
| spellingShingle |
2-s2.0-85019976825 Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| author_facet |
2-s2.0-85019976825 |
| author_sort |
2-s2.0-85019976825 |
| title |
Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| title_short |
Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| title_full |
Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| title_fullStr |
Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| title_full_unstemmed |
Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| title_sort |
Prediction of AC power output in grid-connected photovoltaic system using Artificial Neural Network with multi-variable inputs |
| publishDate |
2017 |
| container_title |
Proceedings - 2016 IEEE Conference on Systems, Process and Control, ICSPC 2016 |
| container_volume |
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| container_issue |
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| doi_str_mv |
10.1109/SPC.2016.7920728 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019976825&doi=10.1109%2fSPC.2016.7920728&partnerID=40&md5=81a26452858fff8c13cf93a4afe0696a |
| description |
This paper presents Artificial Neural Network (ANN) technique for predicting the output power from Grid-Connected Photovoltaic (GCPV) system. Different inputs are utilized in several models of ANN in order to obtain the output power. ANN parameters are chosen using trial-and-error method to find the optimal value of root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R2). The study involved training and testing mode through ANN. Result showed that ANN model 1 (with 6 input data) performs superior result compared to the other models. © 2016 IEEE. |
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Institute of Electrical and Electronics Engineers Inc. |
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| language |
English |
| format |
Conference paper |
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| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1828987879350599680 |