276
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units

&
Pages 6303-6320 | Received 03 Mar 2022, Accepted 29 Jun 2022, Published online: 11 Jul 2022

References

  • Abouelaziz, I., A. Chetouani, M. E. Hassouni, L. J. Latecki, and H. Cherifi. 2018. Convolutional neural network for blind mesh visual quality assessment using 3D visual saliency. 2018 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece, IEEE, 3533–37 doi:10.1109/ICIP.2018.8451763.
  • Alsharif, M. H., M. K. Younes, and J. Kim. 2019. Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry 11 (2):240. doi:10.3390/sym11020240.
  • Atique, S., S. Noureen, V. Roy, V. Subburaj, S. Bayne, and J. Macfie. 2019. Forecasting of total daily solar energy generation using ARIMA: A case study. 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). Las Vegas, NV, USA, IEEE, 0114–0119. doi:10.1109/CCWC.2019.8666481.
  • Bamisile, O., A. Oluwasanmi, S. Obiora, E. Osei-Mensah, G. Asoronye, and Q. Huang. 2020. Application of deep learning for solar irradiance and solar photovoltaic multi-parameter forecast. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–21. doi:10.1080/15567036.2020.1801903.
  • Çelik, Ö., A. Tan, M. Inci, and A. Teke. 2020. Improvement of energy harvesting capability in grid-connected photovoltaic micro-inverters. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–25. doi:10.1080/15567036.2020.1755389.
  • Chu, Y., B. Urquhart, S. M. I. Gohari, H. T. C. Pedro, J. Kleissl, and C. F. M. Coimbra. 2015. Short-term reforecasting of power output from a 48 MWe solar PV plant. Solar Energy 112:68–77. doi:10.1016/j.solener.2014.11.017.
  • Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
  • Dan A, D. J., P. M. Rosa, S. Chakraborty, and T. Senjyu. 2019. Solar pv power prediction using a new approach based on hybrid deep neural network. 2019 IEEE Power & Energy Society General Meeting (PESGM). Atlanta, GA, USA, IEEE, 1–5. doi:10.1109/PESGM40551.2019.8974091.
  • Das, U. K., K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M. Y. I. Idris, W. Van Deventer, B. Horan, and A. Stojcevski. 2018. Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews 81:912–28. doi:10.1016/j.rser.2017.08.017.
  • DKASC Alice Springs. 2021. 1B: Trina. Accessed 01 February 2021. http://dkasolarcentre.com.au/locations/alice-springs?source=1B
  • Du, S., L. Tianrui, X. Gong, and S.-J. Horng. 2018. A hybrid method for traffic flow forecasting using multimodal deep learning. arXiv preprint arXiv:1803.02099.
  • Eseye, A. T., J. Zhang, and D. Zheng. 2018. Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. Renewable Energy 118:357–67. doi:10.1016/j.renene.2017.11.011.
  • Gao, W., and Q. Chen. 2020. An Bayesian learning and nonlinear regression model for photovoltaic power output forecasting. Applied Mathematics and Nonlinear Sciences 5 (2):531–42. doi:10.2478/amns.2020.2.00032.
  • Gundu, V., and S. P. Simon. 2021. Short term solar power and temperature forecast using recurrent neural networks. Neural Processing Letters 53 (6):4407–18. doi:10.1007/s11063-021-10606-7.
  • Hirata, Y., K. Aihara, and H. Suzuki. 2014. Predicting multivariate time series in real time with confidence intervals: Applications to renewable energy. The European Physical Journal Special Topics 223 (12):2451–60. doi:10.1140/epjst/e2014-02210-3.
  • Hirata, Y., and K. Aihara. 2017. Improving time series prediction of solar irradiance after sunrise: Comparison among three methods for time series prediction. Solar Energy 149:294–301. doi:10.1016/j.solener.2017.04.020.
  • Hossain, M., S. Mekhilef, M. Danesh, L. Olatomiwa, and S. Shamshirband. 2017. Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. Journal of Cleaner Production 167:395–405. doi:10.1016/j.jclepro.2017.08.081.
  • Koster, D., F. Minette, C. Braun, and O. O’Nagy. 2019. Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg. Renewable Energy 132:455–70. doi:10.1016/j.renene.2018.08.005.
  • Lee, W., K. Kim, J. Park, J. Kim, and Y. Kim. 2018. Forecasting solar power using long-short term memory and convolutional neural networks. IEEE Access 6:73068–80. doi:10.1109/ACCESS.2018.2883330.
  • Lee, D., and K. Kim. 2021. PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information. Renewable Energy 173:1098–110. doi:10.1016/j.renene.2020.12.021.
  • Li, G., H. Wang, S. Zhang, J. Xin, and H. Liu. 2019. Recurrent neural networks based photovoltaic power forecasting approach. Energies 12 (13):2538. doi:10.3390/en12132538.
  • Li, P., K. Zhou, L. Xinhui, and S. Yang. 2020. A hybrid deep learning model for short-term PV power forecasting. Applied Energy 259:114216. doi:10.1016/j.apenergy.2019.114216.
  • Lin, G.-Q., L. Ling-Ling, M.-L. Tseng, H.-M. Liu, -D.-D. Yuan, and R. R. Tan. 2020. An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. Journal of Cleaner Production 253:119966. doi:10.1016/j.jclepro.2020.119966.
  • Luo, X., D. Zhang, and X. Zhu. 2021. Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge. Energy 225:120240. doi:10.1016/j.energy.2021.120240.
  • Mayer, M. J., and G. Gyula. 2021. Extensive comparison of physical models for photovoltaic power forecasting. Applied Energy 283:116239. doi:10.1016/j.apenergy.2020.116239.
  • Mellit, A., A. Massi Pavan, and V. Lughi. 2021. Deep learning neural networks for short-term photovoltaic power forecasting. Renewable Energy 172:276–88. doi:10.1016/j.renene.2021.02.166.
  • Mukhtar, M., A. Oluwasanmi, N. Yimen, Z. Qinxiu, C. C. Ukwuoma, B. Ezurike, and O. Bamisile. 2022. Development and comparison of two novel hybrid neural network models for hourly solar radiation prediction. Applied Sciences 12 (3):1435. doi:10.3390/app12031435.
  • Mustafa, İ. N. C. İ. 2019. Design and analysis of dual level boost converter based transformerless grid connected PV system for residential applications. 2019 4th International Conference on Power Electronics and their Applications (ICPEA). Elazig, Turkey, IEEE. 1–6.
  • Ozbek, A., A. Yildirim, and M. Bilgili. 2021. Deep learning approach for one-hour ahead forecasting of energy production in a solar-PV plant. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–16. doi:10.1080/15567036.2021.1924316.
  • Pan, M., L. Chao, R. Gao, Y. Huang, H. You, G. Tangsheng, and F. Qin. 2020. Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization. Journal of Cleaner Production 277:123948. doi:10.1016/j.jclepro.2020.123948.
  • Paulescu, M., M. Brabec, R. Boata, and V. Badescu. 2017. Structured, physically inspired (gray box) models versus black box modeling for forecasting the output power of photovoltaic plants. Energy 121:792–802. doi:10.1016/j.energy.2017.01.015.
  • Persson, C., P. Bacher, T. Shiga, and H. Madsen. 2017. Multi-site solar power forecasting using gradient boosted regression trees. Solar Energy 150:423–36. doi:10.1016/j.solener.2017.04.066.
  • Perveen, G., M. Rizwan, N. Goel, and P. Anand. 2020. Artificial neural network models for global solar energy and photovoltaic power forecasting over India. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–26. doi:10.1080/15567036.2020.1826017.
  • Sabri, M., and M. E. Hassouni. 2021. A comparative study of LSTM and RNN for photovoltaic power forecasting. International Conference on Advanced Technologies for Humanity. Rabat, Morocco, 265–74 Springer. doi:10.1007/978-3-030-94188-8_25.
  • Sabri, M., and M. E. Hassouni. 2022. A Novel deep learning approach for short term photovoltaic power forecasting based on GRU-CNN model. In E3S Web of Conferences, Vol. 336, 00064. doi:10.1051/e3sconf/202233600064. EDP Sciences.
  • Sajjad, M., Z. A. Khan, A. Ullah, T. Hussain, W. Ullah, M. Y. Lee, and S. W. Baik. 2020. A novel CNN-GRU-based hybrid approach for short-term residential load forecasting. IEEE Access 8:143759–68. doi:10.1109/ACCESS.2020.3009537.
  • Sharadga, H., S. Hajimirza, and R. S. Balog. 2020. Time series forecasting of solar power generation for large-scale photovoltaic plants. Renewable Energy 150:797–807. doi:10.1016/j.renene.2019.12.131.
  • Singh, B., and D. Pozo. 2019. A guide to solar power forecasting using ARMA models. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). Bucharest, Romania, IEEE. 1–4 doi:10.1109/ISGTEurope.2019.8905430.
  • Wan, C., J. Zhao, Y. Song, X. Zhao, J. Lin, and H. Zechun. 2015. Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems 1 (4):38–46. doi:10.17775/CSEEJPES.2015.00046.
  • Wang, Y., N. Zhang, C. Kang, M. Miao, R. Shi, and Q. Xia. 2017. An efficient approach to power system uncertainty analysis with high-dimensional dependencies. IEEE Transactions on Power Systems 33 (3):2984–94. doi:10.1109/TPWRS.2017.2755698.
  • Wang, Y., W. Liao, and Y. Chang. 2018. Gated recurrent unit network-based short-term photovoltaic forecasting. Energies 11 (8):2163. doi:10.3390/en11082163.
  • Wang, K., Q. Xiaoxia, and H. Liu. 2019a. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Applied Energy 251:113315. doi:10.1016/j.apenergy.2019.113315.
  • Wang, K., Q. Xiaoxia, and H. Liu. 2019b. Photovoltaic power forecasting based LSTM-convolutional network. Energy 189:116225. doi:10.1016/j.energy.2019.116225.
  • Wen, L., K. Zhou, S. Yang, and L. Xinhui. 2019. Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting. Energy 171:1053–65. doi:10.1016/j.energy.2019.01.075.
  • Yagli, G. M., D. Yang, and D. Srinivasan. 2019. Automatic hourly solar forecasting using machine learning models. Renewable and Sustainable Energy Reviews 105:487–98. doi:10.1016/j.rser.2019.02.006.
  • Yildiz, C., and H. Acikgoz. 2021. A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 43 (4):395–412. doi:10.1080/15567036.2020.1801899.
  • Yu, J., X. Zhang, X. Linlin, J. Dong, and L. Zhangzhong. 2021. A hybrid CNN-GRU model for predicting soil moisture in maize root zone. Agricultural Water Management 245:106649. doi:10.1016/j.agwat.2020.106649.
  • Zang, H., L. Cheng, T. Ding, K. W. Cheung, Z. Liang, Z. Wei, and G. Sun. 2018. Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network. IET Generation, Transmission & Distribution 12 (20):4557–67. doi:10.1049/iet-gtd.2018.5847.
  • Zhen, H., D. Niu, K. Wang, Y. Shi, J. Zhengsen, and X. Xiaomin. 2021. Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information. Energy 231:120908. doi:10.1016/j.energy.2021.120908.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.