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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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Autores principales: Kazem, Hussein A., Yousif, Jabar H., Chaichan, Miqdam T., Al-Waeli, Ali H.A., Sopian, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8800028/
https://www.ncbi.nlm.nih.gov/pubmed/35128098
http://dx.doi.org/10.1016/j.heliyon.2022.e08803
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author Kazem, Hussein A.
Yousif, Jabar H.
Chaichan, Miqdam T.
Al-Waeli, Ali H.A.
Sopian, K.
author_facet Kazem, Hussein A.
Yousif, Jabar H.
Chaichan, Miqdam T.
Al-Waeli, Ali H.A.
Sopian, K.
author_sort Kazem, Hussein A.
collection PubMed
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.
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spelling pubmed-88000282022-02-03 Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation Kazem, Hussein A. Yousif, Jabar H. Chaichan, Miqdam T. Al-Waeli, Ali H.A. Sopian, K. Heliyon Research Article 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. Elsevier 2022-01-21 /pmc/articles/PMC8800028/ /pubmed/35128098 http://dx.doi.org/10.1016/j.heliyon.2022.e08803 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Kazem, Hussein A.
Yousif, Jabar H.
Chaichan, Miqdam T.
Al-Waeli, Ali H.A.
Sopian, K.
Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation
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 Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8800028/
https://www.ncbi.nlm.nih.gov/pubmed/35128098
http://dx.doi.org/10.1016/j.heliyon.2022.e08803
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