Forecasting energy production of a PV system connected by using NARX neural network model
Applying artificial neural network techniques to forecast the electricity production of photovoltaic (PV) power plants is a novel concept. A reliable analytical model for calculating the energy output of a grid-connected solar plant is very difficult to establish because of hourly, daily, and season...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-08-01
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| Series: | AIMS Energy |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/energy.2024045 |
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| author | Marwa M. Ibrahim Amr A. Elfeky Amal El Berry |
| author_facet | Marwa M. Ibrahim Amr A. Elfeky Amal El Berry |
| author_sort | Marwa M. Ibrahim |
| collection | DOAJ |
| description | Applying artificial neural network techniques to forecast the electricity production of photovoltaic (PV) power plants is a novel concept. A reliable analytical model for calculating the energy output of a grid-connected solar plant is very difficult to establish because of hourly, daily, and seasonal variations in climate. The current study estimated and predicted the energy production of a connected PV system that was installed in Cairo, Egypt (30.13° N and 31.40 ° E) using an artificial neural network. Four seasons' worth of data (summer, autumn, winter, and spring) were methodically assessed using information from the climate database. The parameters that had an impact on the electrical data of PV modules included meteorological and irradiation variables, energy output, and the user's needs used to verify the NARX feedback neural networks. Prediction performance metrics were obtained, such as the correlation coefficient (R) and root mean square error (RMSE). The observed correlation coefficient ranged from 99% to 100%, indicating that the expected results are verified, while the mean error fluctuates very little. |
| format | Article |
| id | doaj-art-8d8b13c2c3314b82b271d2011a793821 |
| institution | Kabale University |
| issn | 2333-8334 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Energy |
| spelling | doaj-art-8d8b13c2c3314b82b271d2011a7938212025-01-24T01:35:01ZengAIMS PressAIMS Energy2333-83342024-08-0112596898310.3934/energy.2024045Forecasting energy production of a PV system connected by using NARX neural network modelMarwa M. Ibrahim0Amr A. Elfeky1Amal El Berry2Mechanical Engineering Department, Engineering and Renewable Energy Research Institute, National Research Centre (NRC), Cairo 12622, EgyptMechanical Engineering Department, Engineering and Renewable Energy Research Institute, National Research Centre (NRC), Cairo 12622, EgyptMechanical Engineering Department, Engineering and Renewable Energy Research Institute, National Research Centre (NRC), Cairo 12622, EgyptApplying artificial neural network techniques to forecast the electricity production of photovoltaic (PV) power plants is a novel concept. A reliable analytical model for calculating the energy output of a grid-connected solar plant is very difficult to establish because of hourly, daily, and seasonal variations in climate. The current study estimated and predicted the energy production of a connected PV system that was installed in Cairo, Egypt (30.13° N and 31.40 ° E) using an artificial neural network. Four seasons' worth of data (summer, autumn, winter, and spring) were methodically assessed using information from the climate database. The parameters that had an impact on the electrical data of PV modules included meteorological and irradiation variables, energy output, and the user's needs used to verify the NARX feedback neural networks. Prediction performance metrics were obtained, such as the correlation coefficient (R) and root mean square error (RMSE). The observed correlation coefficient ranged from 99% to 100%, indicating that the expected results are verified, while the mean error fluctuates very little.https://www.aimspress.com/article/doi/10.3934/energy.2024045forecastinggrid-connectedpvsystenergy outputnarxneural networks |
| spellingShingle | Marwa M. Ibrahim Amr A. Elfeky Amal El Berry Forecasting energy production of a PV system connected by using NARX neural network model AIMS Energy forecasting grid-connected pvsyst energy output narx neural networks |
| title | Forecasting energy production of a PV system connected by using NARX neural network model |
| title_full | Forecasting energy production of a PV system connected by using NARX neural network model |
| title_fullStr | Forecasting energy production of a PV system connected by using NARX neural network model |
| title_full_unstemmed | Forecasting energy production of a PV system connected by using NARX neural network model |
| title_short | Forecasting energy production of a PV system connected by using NARX neural network model |
| title_sort | forecasting energy production of a pv system connected by using narx neural network model |
| topic | forecasting grid-connected pvsyst energy output narx neural networks |
| url | https://www.aimspress.com/article/doi/10.3934/energy.2024045 |
| work_keys_str_mv | AT marwamibrahim forecastingenergyproductionofapvsystemconnectedbyusingnarxneuralnetworkmodel AT amraelfeky forecastingenergyproductionofapvsystemconnectedbyusingnarxneuralnetworkmodel AT amalelberry forecastingenergyproductionofapvsystemconnectedbyusingnarxneuralnetworkmodel |