Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation
This paper evaluated a 1.4 kW grid-connected photovoltaic system (GCPV) using two neural network models based on experimental data for one year. The novelty of this study is to propose and compare full recurrent neural network (FRNN), and principal component analysis (PCA) models based on entire yea...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022000913 |
| _version_ | 1798018310335365120 |
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| author | Hussein A. Kazem Jabar H. Yousif Miqdam T. Chaichan Ali H.A. Al-Waeli K. Sopian |
| author_facet | Hussein A. Kazem Jabar H. Yousif Miqdam T. Chaichan Ali H.A. Al-Waeli K. Sopian |
| author_sort | Hussein A. Kazem |
| collection | DOAJ |
| description | This paper evaluated a 1.4 kW grid-connected photovoltaic system (GCPV) using two neural network models based on experimental data for one year. The novelty of this study is to propose and compare full recurrent neural network (FRNN), and principal component analysis (PCA) models based on entire year experimental data, considering limited research conducted to predict GCPV behaviour using the two methods. The system data was collected for 12 months secondly and hourly data with 50400 samples daily. The GCPV evaluates using specific yield, energy cost, capacity factor, payback period, current, voltage, power, and efficiency. The predicted GCPV current and power using FRNN and PCA were evaluated and compared with measured values to validate results. However, the results indicated that FRNN is better in simulating the experimental results curve compared with PCA. The measured and predicted data are compared and evaluated. It is found that the GCPV is suitable and promising for the study area in terms of technical and economic evaluation with a 3.24–4.82 kWh/kWp-day yield, 21.7% capacity factor, 0.045 USD/kWh cost of energy, and 11.17 years payback period. |
| first_indexed | 2024-04-11T16:22:04Z |
| format | Article |
| id | doaj.art-498240aa1d214fe6bf36acdb5edba32f |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T16:22:04Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-498240aa1d214fe6bf36acdb5edba32f2022-12-22T04:14:19ZengElsevierHeliyon2405-84402022-01-0181e08803Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generationHussein A. Kazem0Jabar H. Yousif1Miqdam T. Chaichan2Ali H.A. Al-Waeli3K. Sopian4Sohar University, PO Box 44, Sohar, PCI 311, Oman; Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, MalaysiaSohar University, PO Box 44, Sohar, PCI 311, Oman; Corresponding author.Energy and Renewable Energies Technology Research Center, University of Technology-Iraq, IraqSolar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, MalaysiaSolar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, MalaysiaThis paper evaluated a 1.4 kW grid-connected photovoltaic system (GCPV) using two neural network models based on experimental data for one year. The novelty of this study is to propose and compare full recurrent neural network (FRNN), and principal component analysis (PCA) models based on entire year experimental data, considering limited research conducted to predict GCPV behaviour using the two methods. The system data was collected for 12 months secondly and hourly data with 50400 samples daily. The GCPV evaluates using specific yield, energy cost, capacity factor, payback period, current, voltage, power, and efficiency. The predicted GCPV current and power using FRNN and PCA were evaluated and compared with measured values to validate results. However, the results indicated that FRNN is better in simulating the experimental results curve compared with PCA. The measured and predicted data are compared and evaluated. It is found that the GCPV is suitable and promising for the study area in terms of technical and economic evaluation with a 3.24–4.82 kWh/kWp-day yield, 21.7% capacity factor, 0.045 USD/kWh cost of energy, and 11.17 years payback period.http://www.sciencedirect.com/science/article/pii/S2405844022000913Grid connected PVRecurrent neuralPrincipal component analysisDesert type PVANN |
| spellingShingle | Hussein A. Kazem Jabar H. Yousif Miqdam T. Chaichan Ali H.A. Al-Waeli K. Sopian Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation Heliyon Grid connected PV Recurrent neural Principal component analysis Desert type PV ANN |
| title | Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation |
| title_full | Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation |
| title_fullStr | Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation |
| title_full_unstemmed | Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation |
| title_short | Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation |
| title_sort | long term power forecasting using frnn and pca models for calculating output parameters in solar photovoltaic generation |
| topic | Grid connected PV Recurrent neural Principal component analysis Desert type PV ANN |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022000913 |
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