Photovoltaic Power Forecasting based on Artificial Neural Network and Ultraviolet Index
Keywords:PV power forecasting, artificial neural network, backpropagation neural network, UV index, Adam optimizer, Keras-tuner hyperparameter optimization
The accuracy of photovoltaic (PV) power generation forecast can seriously affect the penetration ability of PV power into the existing power grid, which is one of the key approaches to achieve emission peak, as well as realize carbon neutrality. In the conventional forecasting methods, Global Horizontal Irradiation (GHI), Diffuse Horizontal Irradiance (DHI), temperature, wind speed, rainfall, etc. are considered as the mainly factors to forecast the PV output power, but ignore the impact of PV power generation caused by the whole PV system’s decay over the 25–30 years lifecycle. The ultraviolet (UV) index, which reflects the quantity of 10–400 nm irradiation, has a strong correlation with such decay and power generation. This paper proposes a novel PV power forecasting model that involving UV index in an artificial neural network, using Adam method to optimize the training process with the Keras-tuner employed for optimization of the hyperparameters. Experiments demonstrate that the proposed model achieves more precise performance than conventional methods.
G. Stapleton, S. Neill, Grid-Connected Solar Electric Systems, first ed., Routledge, London, 2012, pp. 35-40. https://doi.org/10.4324/9780203588628.
M. Cococcioni, E. D’Andrea and B. Lazzerini, “24-hour-ahead forecasting of energy production in solar PV system,” Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, Cordoba, Spain, 22-24 Nov. 2011, pp. 1276-1281. https://doi.org/10.1109/ISDA.2011.6121835.
H. Sheng, Jian Xiao, Yuhua Cheng, Qiang Ni, Song Wang, “Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression,” IEEE Transactions on Industrial Electronics, vol. 65, issue 1, pp. 300-308, 2018. https://doi.org/10.1109/TIE.2017.2714127.
E. Lorenz, J. Hurka, D. Heinemann, H. G. Beyer, “Irradiance forecasting for the power prediction of grid-connected photovoltaic systems,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 2, issue 1, pp. 2-10, 2009. https://doi.org/10.1109/JSTARS.2009.2020300.
E. Geraldi, F. Romano, E. Ricciardelli, “An advanced model for the estimation of the surface solar irradiance under all atmospheric conditions using MSG/SEVIRI data,” IEEE Trans Geosci Remote Sens, vol. 50, issue 8, pp. 2934–2953, 2012. https://doi.org/10.1109/TGRS.2011.2178855.
S. H. Oudjana, A. Hellal and I. H. Mahamed, “Short term photovoltaic power generation forecasting using neural network,” Proceedings of the 2012 11th International Conference on Environment and Electrical Engineering, Italy, May 18-25, 2012, pp. 706-711. https://doi.org/10.1109/EEEIC.2012.6221469.
L. Hernandez, C. Baladrón, J. M. Aguiar, B. Carro, A. J. Sanchez-Esguevillas and J. Lloret, “Short-term load forecasting for microgrids based on artificial neural networks,” Energies, vol. 2013, pp. 1385-1408, 2013. https://doi.org/10.3390/en6031385.
A. Badiee, R. Wildman and I. Ashcroft, “Effect of UV aging on degradation of Ethylene-vinyl Acetate (EVA) as encapsulant in photovoltaic (PV) modules,” Proceedings of SPIE – The International Society for Optical Engineering, 9179, USA, October 8, 2014. https://doi.org/10.1117/12.2062007.
H. T. Yang, C. M. Huang, Y. C. Huang and Yi-Shiang Pai, “A weatherbased hybrid method for 1- day ahead hourly forecasting of PV power output,” IEEE Transactions on Sustainable Energy, vol. 5, no. 3, pp. 917-926, 2014. https://doi.org/10.1109/TSTE.2014.2313600.
S. P. Mouri, S. N. Sakib, S. Hoque, M. S. Kaiser, “Theoretical efficiency and cell parameters of AlAs/GaAs/Ge based new multijunction solar cell,” Proceedings of the iCEEiCT 2016, Bangladesh, September 22-24, 2016, pp. 1-6. https://doi.org/10.1109/CEEICT.2016.7873128.
G. S. Kinsey, Z. Energy, “Solar cell efficiency divergence due to operating spectrum variation,” Solar Energy, vol. 217, pp. 49-57, 2021. https://doi.org/10.1016/j.solener.2021.01.024.
K. Shiruru, “An introduction to artificial neural network,” International Journal of Advance Research and Innovative Ideas in Education, vol. 1, issue 5, pp.27-30, 2016.
S. J. Russell and P. Norvig, Artificial Intelligence a Modern Approach, third ed, Pearson, New York, 2010, p. 1151.
A. Ali-Hameed, B. Karlik, M. S. Salman, “Back-propagation algorithm with variable adaptive momentum,” Knowledge-Based Systems, vol. 114, 2016. https://doi.org/10.1016/j.knosys.2016.10.001.
K. Eckle, J. Schmidt-Hieber, “A comparison of deep networks with ReLU activation function and linear spline-type methods,” Neural Networks, vol. 110, pp. 232–242, 2019. https://doi.org/10.1016/j.neunet.2018.11.005.
D. P. Kingma and J. L. Ba, “ADAM: A Method For Stochastic Optimization,” Proceedings of the The 3rd International Conference for Learning Representations, USA, May 7-9, 2015.
D. S. Abdelminaam, F. H. Ismail, M. Taha, A. Taha, E. H. Houssein, A. Nabil, “CoAID-DEEP: An optimized intelligent framework for automated detecting COVID-19 misleading information on Twitter,” IEEE Access, vol. 9, pp. 27840–27867, 2021. https://doi.org/10.1109/ACCESS.2021.3058066.
T.-H. Wang, X. Hu, H. Jin, Q. Song, X. Han, Z. Liu, “AutoRec: An automated recommender system share on,” Proceedings of the Fourteenth ACM Conference on Recommender Systems, September 22-26, 2020, pp. 582-584. https://doi.org/10.1145/3383313.3411529.
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar, “Hyperband: A novel bandit-based approach to hyperparameter optimization,” The Journal of Machine Learning Research, vol. 18, issue 1, pp. 6765–6816, 2017.
A. F. Rogachev and E. V. Melikhova, “Automation of the process of selecting hyperparameters for artificial neural networks for processing retrospective text information,” IOP Conference Series: Earth and Environmental Science, vol. 577, May 10, 2020. https://doi.org/10.1088/1755-1315/577/1/012012.
Alice spring of Desert Knowledge Australia Centre. [Online]. Available at: http://dkasolarcentre.com.au/historical-data/download.
Dataset created by Australian Radiation Protection and Nuclear Safety Agency (ARPANSA). [Online]. Available at: data.gov.au.
M. T. Hagan, H. B. Demuth, M. H. Beale, O. De Jesús, Neural Network Design, second ed, Martin Hagan, 2014, 800 p.
A. Ng, Machine Learning Yearning, draft version, deepinglearning.ai, 2018, 118 p.
H. K. Yadav, Y. Pal, M. M. Tripathi, “Photovoltaic power forecasting methods in smart power grid,” Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India, December 17-20, 2015, pp. 734-739. ttps://doi.org/10.1109/INDICON.2015.7443522.
A. Dolara, F. Grimaccia, S. Leva, M. Mussetta and E. Ogliari, “A physical hybrid artificial neural network for short term forecasting of PV plant power output,” Energies, vol. 8, pp. 1138-1153, 2015. https://doi.org/10.3390/en8021138.
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