Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

Abstract

The need for electrical energy has increased considerably due to technological developments. Reducing costs and losses, especially in the supply of electrical energy, is among the goals of energy companies. Photovoltaic energy has been an important alternative in reducing energy costs. However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid. Therefore, the generated photovoltaic energy needs to be accurately estimated to be transferred to the grid smoothly. In the literature, many forecasting models have been used for photovoltaic power forecasting. Each of these forecasting models has estimated photovoltaic power using different input parameters, different estimation intervals, and different estimation algorithms. This paper was conducted using the Teaching-Learning Based Optimization (TLBO) algorithm as an alternative approach to photovoltaic power forecasting models. According to the forecasting results, the root mean square error (RMSE) for the test subset was obtained as 270.32 kW, and the mean absolute percentage error (MAPE) was found to be 3.87%. These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

Keywords

• Photovoltaic Power
• Current Developments
• Photovoltaic Power Estimation
• TLBO

References

1. Amarasinghe, G., & Abeygunawardane, S. (2018). An artificial neural network for solar power generation forecasting using weather parameters. In: Proceedings of the 112th Annual Sessions, Institution of Engineers Sri Lanka, (pp. 431-438), Colombo, Sri Lanka.
2. Cheng, Z., Liu, Q., & Xing, Y. (2019). A hybrid probabilistic estimation method for photovoltaic power generation forecasting. Energy Procedia, 158, 173-178. http://www.doi.org/10.1016/j.egypro.2019.01.066
3. Dandıl, E., & Gürgen, E. (2019). Yapay Sinir Ağları Kullanılarak Fotovoltaik Panel Güç Çıkışlarının Tahmini ve Sezgisel Algoritmalar ile Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, 16, 146-158. http://www.doi.org/10.31590/ejosat.540262
4. Das, S. (2021). Short term forecasting of solar radiation and power output of 89.6 kWp solar PV power plant. Materials Today: Proceedings, 39, 1959-1969. http://www.doi.org/10.1016/j.matpr.2020.08.449
5. Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Deventer, W. V., Horan, B. & Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 81, 912-928. http://www.doi.org/10.1016/j.rser.2017.08.017
6. Dosdoğru, A. T., & İpek, A. B. (2022). Hybrid boosting algorithms and artificial neural network for wind speed prediction. International Journal of Hydrogen Energy, 47(3), 1449-1460. http://www.doi.org/10.1016/j.ijhydene.2021.10.154
7. Elsinga, B., & Van Sark, W. G. J. H. M. (2017). Short-term peer-to-peer solar forecasting in a network of photovoltaic systems. Applied Energy, 206, 1464-1483. http://www.doi.org/10.1016/j.apenergy.2017.09.115
8. Han, Y., Wang, N., Ma, M., Zhou, H., Dai, S., & Zhu, H. (2019). A PV power interval forecasting based on seasonal model and nonparametric estimation algorithm. Solar Energy, 184, 515-526. http://www.doi.org/10.1016/j.solener.2019.04.025

9. IRENA, International Renewable Energy Agency. (2024). Renewable capacity highlights. (Accessed:04/09/2024) https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2024/Mar/IRENA_RE_Capacity_Highlights_2024.pdf?rev=7692ae29458142dd8563618f496e0abb
10. Irmak, E., Yesilbudak, M., & Tasdemir, O. (2023, June 4-7). Daily prediction of PV power output using particulate matter parameter with artificial neural networks. In: Proceedings of the 11th International Conference on Smart Grid (icSmartGrid), (pp. 499-502). Paris, France. https://doi.org/10.1109/icSmartGrid58556.2023.10171103
11. Irmak, E., Yeşilbudak, M., & Taşdemir, O. (2024). Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model. Electric Power Components and Systems, 52(11), 1998-2007. http://www.doi.org/10.1080/15325008.2024.2322668
12. Korkmaz, D. (2021). SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting. Applied Energy, 300, 117410. http://www.doi.org/10.1016/j.apenergy.2021.117410
13. Kleissl, J. (2013). Solar energy forecasting and resource assessment. Academic Press.
14. Li, P., Zhou, K., Lu, X., & Yang, S. (2020). A hybrid deep learning model for short-term PV power forecasting. Applied Energy, 259, 114216. http://www.doi.org/10.1016/j.apenergy.2019.114216
15. Liang, L., Su, T., Gao, Y., Qin, F., & Pan, M. (2023). FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation. Journal of Cleaner Production, 385, 135716. http://www.doi.org/10.1016/j.jclepro.2022.135716
16. Lin, W., Zhang, B., Li, H., & Lu, R. (2022). Multi-step prediction of photovoltaic power based on two-stage decomposition and BILSTM. Neurocomputing, 504, 56-67. http://www.doi.org/10.1016/j.neucom.2022.06.117
17. Liu, L., Zhao, Y., Chang, D., Xie, J., Ma, Z., Sun, Q., Yin, H., & Wennersten, R. (2018). Prediction of short-term PV power output and uncertainty analysis. Applied Energy, 228, 700-711. http://www.doi.org/10.1016/j.apenergy.2018.06.112
18. Ma, X., & Zhang, X. (2022). A short-term prediction model to forecast power of photovoltaic based on MFA-Elman. Energy Reports, 8, 495-507. http://www.doi.org/10.1016/j.egyr.2022.01.213
19. Maghami, M. R., Hizam, H., Gomes, C., Radzi, M. A., Rezadad, M. I., & Hajighorbani, S. (2016). Power loss due to soiling on solar panel: A review. Renewable and Sustainable Energy Reviews, 59, 1307-1316. http://www.doi.org/10.1016/j.rser.2016.01.044
20. Moreira, M. O., Balestrassi, P. P., Paiva, A. P., Ribeiro, P. F., & Bonatto, B. D. (2021). Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting. Renewable and Sustainable Energy Reviews, 135, 110450. http://www.doi.org/10.1016/j.rser.2020.110450
21. Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720. http://www.doi.org/10.1016/j.scient.2012.12.005
22. Rao, R. V., Savsani, V. J., & Balic, J. (2012). Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, 44(12), 1447-1462. http://www.doi.org/10.1080/0305215X.2011.652103
23. Qu, Y., Xu, J., Sun, Y., & Liu, D. (2021). A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting. Applied Energy, 304, 117704. http://www.doi.org/10.1016/j.apenergy.2021.117704
24. Saber, E. M., Lee, S. E., Manthapuri, S., Yi, W., & Deb, C. (2014). PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings. Energy, 71, 588-595. http://www.doi.org/10.1016/j.energy.2014.04.115
25. Tang, Y., Yang, K., Zhang, S., & Zhang, Z. (2022). Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy. Renewable and Sustainable Energy Reviews, 162, 112473. http://www.doi.org/10.1016/j.rser.2022.112473
26. Theocharides, S., Makrides, G., Livera, A., Theristis, M., Kaimakis, P., & Georghiou, G. E. (2020). Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing. Applied Energy, 268, 115023. http://www.doi.org/10.1016/j.apenergy.2020.115023
27. VanDeventer, W., Jamei, E., Thirunavukkarasu, G. S., Seyedmahmoudian, M., Soon, T. K., Horan, B., Mekhilef. S., & Stojcevski, A. (2019). Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 140, 367-379. http://www.doi.org/10.1016/j.renene.2019.02.087