How to cite this paper
Zeng, W. (2024). Artificial neural network modeling of solar photovoltaic panel energy output.Journal of Future Sustainability, 4(3), 149-158.
Refrences
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Renno, C., Petito, F., & Gatto, A. (2016). ANN model for predicting the direct normal irradiance and the global radia-tion for a solar application to a residential building. Journal of Cleaner Production, 135(1), 1298-1316.
Solangi, K., Islam, M., & Saidur, R. (2011). A review on global solar energy policy. Renewable and Sustainable Energy Reviews, 15(4), 2149-2163.
Srivastava, N. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.
Touati, F., & Al-Hitmi, M. (2013). Study of the Effects of Dust, Relative Humidity, and Temperature on Solar PV Per-formance in Doha: Comparison Between Monocrystalline and Amorphous PVS. International Journal of Green En-ergy, 10(7), 680-689.
Tsoukpoe, K. (2022). Effect of orientation and tilt angles of solar collectors on their performance: Analysis of the rele-vance of general recommendations in the West and Central African context, Scientific African, 15, 1-20.
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Zazoum, B. (2022). Solar photovoltaic power prediction using different machine learning methods. Energy Reports, 8 , 19-25.
“California Solar.” Solar Energy Industrial Association. www.seia.org/state-solar-policy.
“Net Zero by 2050.” IEA. www.iea.org/reports/net-zero-by-2050.
Ahmed, R., & Sreeram, V. (2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: Tech-niques and optimization. Renew Sustain Energy Rev, 124, 1-26.
Caldas, M., & Alonso, R. (2019). Very short-term solar irradiance forecast using all-sky imaging and real-time irradi-ance measurements. Renewable Energy, 143, 1643-1658.
Chang, R., Bai, L., & Hsu, C. (2021). Solar power generation prediction based on deep Learning. Sustainable Energy Technologies and Assessments, 47, 1-8.
Chung, K. (2018). Effect of diffuse solar radiation on the thermal performance of solar collectors. Case Studies in Thermal Engineering, 12, 759-794.
Cornelia, A., Fjelkestam, F., & Zuansi, C., (2022). Novel machine learning approach for solar photovoltaic energy out-put forecast using extra-terrestrial solar irradiance. Applied Energy, 306(15), 1-11.
Kingma, D.P. (2023). ADAM: A method for stochastic optimization. www.arxiv.org/pdf/1412.6980.pdf. Accessed on Ju-ly 10th 2023.
Esfahani, S. (2021). Optimizing the solar energy capture of residential roof design in the southern hemisphere through Evolutionary Algorithm. Energy and Built Environment, 2(4), 406-424.
Geethaa, A., & Santhakumar, J. (2022). Prediction of hourly solar radiation in Tamil Nadu using ANN model with dif-ferent learning algorithms. Energy Reports, 8(1), 664-671.
Guijo, D., & Duran, A. (2020). Evolutionary artificial neural networks for accurate solar radiation prediction. Energy, 210(1), 1-11.
Hasicic, M., Bilic, D., & Siljak, H. (2017). Criteria for Solar Car Optimized Route Estimation. Microprocessors and Microsystems, 51, 189-296.
Lave, M., & Kleissl, J. (2013). Quantifying and simulating solar-plant variability using irradiance data. in: J. Kleissl (Ed.), Solar Energy Forecasting and Resource Assessment. Academic Press, Boston, 149-169.
Meer, D., & Shepero, M. (2018). Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes. Applied Energy, 213(1), 195-207.
Njok, A. (2020). The influence of solar power and solar flux on the efficiency of polycrystalline photovoltaics installed close to a river. Indonesian Journal of Electrical Engineering and Computer Science, 17(2), 988-996.
Nordell, B. (2003). Thermal pollution causes global warming. Glob Planet Change, 38(3), 305-312.
Owusu, P., & AsuMAEusarkodie, S., (2016). A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng, 3(1), 1-14.
Renno, C., Petito, F., & Gatto, A. (2016). ANN model for predicting the direct normal irradiance and the global radia-tion for a solar application to a residential building. Journal of Cleaner Production, 135(1), 1298-1316.
Solangi, K., Islam, M., & Saidur, R. (2011). A review on global solar energy policy. Renewable and Sustainable Energy Reviews, 15(4), 2149-2163.
Srivastava, N. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.
Touati, F., & Al-Hitmi, M. (2013). Study of the Effects of Dust, Relative Humidity, and Temperature on Solar PV Per-formance in Doha: Comparison Between Monocrystalline and Amorphous PVS. International Journal of Green En-ergy, 10(7), 680-689.
Tsoukpoe, K. (2022). Effect of orientation and tilt angles of solar collectors on their performance: Analysis of the rele-vance of general recommendations in the West and Central African context, Scientific African, 15, 1-20.
Weather and Solar information. www.visualcrossing.com. Accessed on July 14th 2023.
Zazoum, B. (2022). Solar photovoltaic power prediction using different machine learning methods. Energy Reports, 8 , 19-25.
“California Solar.” Solar Energy Industrial Association. www.seia.org/state-solar-policy.
“Net Zero by 2050.” IEA. www.iea.org/reports/net-zero-by-2050.