An Approach to Optimized the Output Power of Photovoltaic System Using Artificial Neural Networks
Yıl 2024,
Cilt: 13 Sayı: 1, 18 - 30, 12.06.2024
Alkhansa Osman Mohammed Abdalla
,
Güven Önbilgin
Öz
With the remarkable development of technology, the global energy crisis and green technology have boosted demand for the utilization of renewable energy sources, and energy storage technology as an appealing solution to enhance power efficiency. The paper describes strategies to improve energy management efficiency by ensuring the best selection of photovoltaic (PV) solar cell characteristics while constructing a solar energy network. An artificial neural network (ANN) algorithm is integrated with the capabilities of the PVsyst6.8.5 simulation program to discover accurate value forecasts that can be utilized to achieve the best degree of effectiveness of output power values while using the energy solar system. This developed model applies open-source data to estimate the ideal tilt angle for producing maximum power from the PV module. It was demonstrated that the suggested approach, employing the Neural Networks algorithm with PVsyst 6.8.5, can efficiently estimate the predicted output power.
Kaynakça
- Adhiparasakthi Engineering College. Department of Electrical and Electronics Engineering, Institute of Electrical and Electronics Engineers. Madras Section, & Institute of Electrical and Electronics Engineers. (n.d.). 2015 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC).
- Arslan, S., Esen, H., Avci, E., & Cengiz, C. (2023). Modeling a Solar Power Plant with Artificial Neural Networks. International Journal of Innovative Engineering Applications. https://doi.org/10.46460/ijiea.1336917
- Bermejo, J. F., Fernández, J. F. G., Polo, F. O., & Márquez, A. C. (2019). A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. In Applied Sciences (Switzerland) (Vol. 9, Issue 9). MDPI AG. https://doi.org/10.3390/app9091844
- Ceylan, I., Erkaymaz, O., Gedik, E., & Gurel, A. E. (2014). The prediction of photovoltaic module temperature with artificial neural networks. Case Studies in Thermal Engineering, 3, 11–20. https://doi.org/10.1016/j.csite.2014.02.001
- Chandrasekaran, K., Selvaraj, J., Amaladoss, C. R., & Veerapan, L. (2021). Hybrid renewable energy based smart grid system for reactive power management and voltage profile enhancement using artificial neural network. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 43(19), 2419–2442. https://doi.org/10.1080/15567036.2021.1902430
- EEE 8005-Student Directed Learning (SDL) EEE 8005-Student Directed Learning (SDL) Industrial Automation-Artificial Neural networks Industrial Automation-Artificial Neural networks Written by: Shady Gadoue Written by: Shady Gadoue. (n.d.).
- Heidari, M. (2016). Improving Efficiency of Photovoltaic System by Using Neural Network MPPT and Predictive Control of Converter. In INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH M.Heidari (Vol. 6, Issue 4).
- Jordehi, A. R. (2016). Parameter estimation of solar photovoltaic (PV) cells: A review. Renewable and Sustainable Energy Reviews, 61, 354–371. https://doi.org/10.1016/j.rser.2016.03.049
- Li, S., Gong, W., & Gu, Q. (2021). A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. Renewable and Sustainable Energy Reviews, 141(May 2020), 110828. https://doi.org/10.1016/j.rser.2021.110828
- Lo Brano, V., Ciulla, G., & Di Falco, M. (2014). Artificial neural networks to predict the power output of a PV panel. International Journal of Photoenergy, 2014. https://doi.org/10.1155/2014/193083
- Marouf, A. (2018). ANN for Predicting Antibiotic Susceptibility. In International Journal of Academic Pedagogical Research (Vol. 2). www.ijeais.org/ijapr
- Pawar, P., TarunKumar, M., & Vittal K., P. (2020). An IoT based Intelligent Smart Energy Management System with accurate forecasting and load strategy for renewable generation. Measurement: Journal of the International Measurement Confederation, 152. https://doi.org/10.1016/j.measurement.2019.107187
- Peinado Gonzalo, A., Pliego Marugán, A., & García Márquez, F. P. (2020). Survey of maintenance management for photovoltaic power systems. Renewable and Sustainable Energy Reviews, 134(July). https://doi.org/10.1016/j.rser.2020.110347
- Purwanto, Hermawan, Suherman, Widodo, D. A., & Iksan, N. (2021, April 28). Renewable Energy Generation Forecasting on Smart Home Micro Grid using Deep Neural Network. AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems. https://doi.org/10.1109/AIMS52415.2021.9466089
- Rauf, S., Wahab, A., Rizwan, M., Rasool, S., & Khan, N. (2016). Application of dc-grid for Efficient use of solar PV System in Smart Grid. Procedia Computer Science, 83, 902–906. https://doi.org/10.1016/j.procs.2016.04.182
- Şahin, M. (2019). Determining optimum Tilt angles of photovoltaic panels by using artificial neural networks in Turkey. Tehnicki Vjesnik, 26(3), 596–602. https://doi.org/10.17559/TV-20160702220418
- Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2016). Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems, 1(4), 38–46. https://doi.org/10.17775/cseejpes.2015.00046
- Yang, Z., & Xiao, Z. (2023). A Review of the Sustainable Development of Solar Photovoltaic Tracking System Technology. In Energies (Vol. 16, Issue 23). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/en16237768
An Approach to Optimized the Output Power of Photovoltaic System Using Artificial Neural Networks
Yıl 2024,
Cilt: 13 Sayı: 1, 18 - 30, 12.06.2024
Alkhansa Osman Mohammed Abdalla
,
Güven Önbilgin
Öz
With the remarkable development of technology, the global energy crisis and green technology have boosted demand for the utilization of renewable energy sources, and energy storage technology as an appealing solution to enhance power efficiency. The paper describes strategies to improve energy management efficiency by ensuring the best selection of photovoltaic (PV) solar cell characteristics while constructing a solar energy network. An artificial neural network (ANN) algorithm is integrated with the capabilities of the PVsyst6.8.5 simulation program to discover accurate value forecasts that can be utilized to achieve the best degree of effectiveness of output power values while using the energy solar system. This developed model applies open-source data to estimate the ideal tilt angle for producing maximum power from the PV module. It was demonstrated that the suggested approach, employing the Neural Networks algorithm with PVsyst 6.8.5, can efficiently estimate the predicted output power.
Kaynakça
- Adhiparasakthi Engineering College. Department of Electrical and Electronics Engineering, Institute of Electrical and Electronics Engineers. Madras Section, & Institute of Electrical and Electronics Engineers. (n.d.). 2015 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC).
- Arslan, S., Esen, H., Avci, E., & Cengiz, C. (2023). Modeling a Solar Power Plant with Artificial Neural Networks. International Journal of Innovative Engineering Applications. https://doi.org/10.46460/ijiea.1336917
- Bermejo, J. F., Fernández, J. F. G., Polo, F. O., & Márquez, A. C. (2019). A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. In Applied Sciences (Switzerland) (Vol. 9, Issue 9). MDPI AG. https://doi.org/10.3390/app9091844
- Ceylan, I., Erkaymaz, O., Gedik, E., & Gurel, A. E. (2014). The prediction of photovoltaic module temperature with artificial neural networks. Case Studies in Thermal Engineering, 3, 11–20. https://doi.org/10.1016/j.csite.2014.02.001
- Chandrasekaran, K., Selvaraj, J., Amaladoss, C. R., & Veerapan, L. (2021). Hybrid renewable energy based smart grid system for reactive power management and voltage profile enhancement using artificial neural network. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 43(19), 2419–2442. https://doi.org/10.1080/15567036.2021.1902430
- EEE 8005-Student Directed Learning (SDL) EEE 8005-Student Directed Learning (SDL) Industrial Automation-Artificial Neural networks Industrial Automation-Artificial Neural networks Written by: Shady Gadoue Written by: Shady Gadoue. (n.d.).
- Heidari, M. (2016). Improving Efficiency of Photovoltaic System by Using Neural Network MPPT and Predictive Control of Converter. In INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH M.Heidari (Vol. 6, Issue 4).
- Jordehi, A. R. (2016). Parameter estimation of solar photovoltaic (PV) cells: A review. Renewable and Sustainable Energy Reviews, 61, 354–371. https://doi.org/10.1016/j.rser.2016.03.049
- Li, S., Gong, W., & Gu, Q. (2021). A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. Renewable and Sustainable Energy Reviews, 141(May 2020), 110828. https://doi.org/10.1016/j.rser.2021.110828
- Lo Brano, V., Ciulla, G., & Di Falco, M. (2014). Artificial neural networks to predict the power output of a PV panel. International Journal of Photoenergy, 2014. https://doi.org/10.1155/2014/193083
- Marouf, A. (2018). ANN for Predicting Antibiotic Susceptibility. In International Journal of Academic Pedagogical Research (Vol. 2). www.ijeais.org/ijapr
- Pawar, P., TarunKumar, M., & Vittal K., P. (2020). An IoT based Intelligent Smart Energy Management System with accurate forecasting and load strategy for renewable generation. Measurement: Journal of the International Measurement Confederation, 152. https://doi.org/10.1016/j.measurement.2019.107187
- Peinado Gonzalo, A., Pliego Marugán, A., & García Márquez, F. P. (2020). Survey of maintenance management for photovoltaic power systems. Renewable and Sustainable Energy Reviews, 134(July). https://doi.org/10.1016/j.rser.2020.110347
- Purwanto, Hermawan, Suherman, Widodo, D. A., & Iksan, N. (2021, April 28). Renewable Energy Generation Forecasting on Smart Home Micro Grid using Deep Neural Network. AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems. https://doi.org/10.1109/AIMS52415.2021.9466089
- Rauf, S., Wahab, A., Rizwan, M., Rasool, S., & Khan, N. (2016). Application of dc-grid for Efficient use of solar PV System in Smart Grid. Procedia Computer Science, 83, 902–906. https://doi.org/10.1016/j.procs.2016.04.182
- Şahin, M. (2019). Determining optimum Tilt angles of photovoltaic panels by using artificial neural networks in Turkey. Tehnicki Vjesnik, 26(3), 596–602. https://doi.org/10.17559/TV-20160702220418
- Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2016). Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems, 1(4), 38–46. https://doi.org/10.17775/cseejpes.2015.00046
- Yang, Z., & Xiao, Z. (2023). A Review of the Sustainable Development of Solar Photovoltaic Tracking System Technology. In Energies (Vol. 16, Issue 23). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/en16237768