Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review

Irfan Mujahidin; Fikri Arif Rakhman

doi:10.30591/jpit.v9i2.7051

Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review

Irfan Mujahidin, Fikri Arif Rakhman

Abstract

Solar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machine learning models, some are single models and others are hybrid models. Optimization algorithms are used to optimize parameters and improve the prediction accuracy of machine learning models. This research reviews fifteen journals that have been filtered to obtain those discussing optimization algorithms in the predictive models of solar panel output power. This journal will examine the optimization algorithms used in machine learning models for predicting solar panel output power, discussing various types of optimization algorithms, their application in machine learning models, the prediction results from these models, the input data used, and the data collection locations that significantly influence the prediction outcomes. From the results of this research, it does not conclude which machine learning model is the best, due to the many factors that influence it. However, this research is expected to provide references on the application of machine learning models in predicting the output power of solar panels, thereby encouraging the use of renewable energy sources.

Keywords

Optimization Algorithm, Machine Learning, Power Output Prediction, Photovoltaic

Full Text:

References

K. Bot, A. Ruano, and M. G. Da Ruano, “Short-term forecasting photovoltaic solar power for home energy management systems,” Inventions, vol. 6, no. 1, pp. 1–23, Mar. 2021, doi: 10.3390/inventions6010012.

S. C. Lim, J. H. Huh, S. H. Hong, C. Y. Park, and J. C. Kim, “Solar Power Forecasting Using CNN-LSTM Hybrid Model,” Energies, vol. 15, no. 21, Nov. 2022, doi: 10.3390/en15218233.

M. Kermia, D. Abbes, and J.Bosche, Photovoltaic power prediction using a recurrent neural network RNN, 6th ed. IEEE International Energy Conference (ENERGYCON), 2020.

M. S. Hossain and H. Mahmood, “Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast,” IEEE Access, vol. 8, pp. 172524–172533, 2020, doi: 10.1109/ACCESS.2020.3024901.

G. Li, S. Xie, B. Wang, J. Xin, Y. Li, and S. Du, “Photovoltaic Power Forecasting with a Hybrid Deep Learning Approach,” IEEE Access[1] G. Li, S. Xie, B. Wang, J. Xin, Y. Li, S. Du, “Photovoltaic Power Forecast. with a Hybrid Deep Learn. Approach,” IEEE Access, vol. 8, pp. 175871–175880, 2020, doi 10.1109/ACCESS.2020.3025860., vol. 8, pp. 175871–175880, 2020, doi: 10.1109/ACCESS.2020.3025860.

M. Massaoudi, I. Chihi, H. Abu-Rub, S. S. Refaat, and F. S. Oueslati, “Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review,” IEEE Access, vol. 9, pp. 136593–136615, 2021, doi: 10.1109/ACCESS.2021.3117004.

M. Cordeiro-Costas, D. Villanueva, P. Eguía-Oller, and E. Granada-Álvarez, “Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting,” Appl. Sci., vol. 12, no. 17, Sep. 2022, doi: 10.3390/app12178769.

M. Konstantinou, S. Peratikou, and A. G. Charalambides, “Solar photovoltaic forecasting of power output using lstm networks,” Atmosphere (Basel)., vol. 12, no. 1, pp. 1–17, 2021, doi: 10.3390/atmos12010124.

S. Cantillo-Luna, R. Moreno-Chuquen, D. Celeita, and G. Anders, “Deep and Machine Learning Models to Forecast Photovoltaic Power Generation,” Energies, vol. 16, no. 10, May 2023, doi: 10.3390/en16104097.

A. Abubakar Mas’ud, “Comparison of three machine learning models for the prediction of hourly PV output power in Saudi Arabia,” Ain Shams Eng. J., vol. 13, no. 4, p. 101648, 2022, doi: 10.1016/j.asej.2021.11.017.

H. Guo, J. Zhou, M. Koopialipoor, D. Jahed Armaghani, and M. M. Tahir, “Deep neural network and whale optimization algorithm to assess flyrock induced by blasting,” Eng. Comput., vol. 37, no. 1, pp. 173–186, 2021, doi: 10.1007/s00366-019-00816-y.

V. Kusuma, A. Privadi, A. L. Setya Budi, and V. L. Budiharto Putri, “Photovoltaic Power Forecasting Using Recurrent Neural Network Based on Bayesian Regularization Algorithm,” in ICPEA 2021 - 2021 IEEE International Conference in Power Engineering Application, Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 109–114. doi: 10.1109/ICPEA51500.2021.9417833.

Z. M. Yasin, N. A. Salim, N. F. A. Aziz, H. Mohamad, and N. A. Wahab, “Prediction of solar irradiance using grey Wolf optimizer least square support vector machine,” Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 1, pp. 10–17, 2019, doi: 10.11591/ijeecs.v17.i1.pp10-17.

B. Gu, H. Shen, X. Lei, H. Hu, and X. Liu, “Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method,” Appl. Energy, vol. 299, no. June, 2021, doi: 10.1016/j.apenergy.2021.117291.

D. Liu and K. Sun, “Random forest solar power forecast based on classification optimization,” Energy, vol. 187, p. 115940, 2019, doi: 10.1016/j.energy.2019.115940.

M. Pan et al., “Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization,” J. Clean. Prod., vol. 277, Dec. 2020, doi: 10.1016/j.jclepro.2020.123948.

G. Q. Lin, L. L. Li, M. L. Tseng, H. M. Liu, D. D. Yuan, and R. R. Tan, “An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation,” J. Clean. Prod., vol. 253, p. 119966, 2020, doi: 10.1016/j.jclepro.2020.119966.

Y. W. Liu, H. Feng, H. Y. Li, and L. L. Li, “An improved whale algorithm for support vector machine prediction of photovoltaic power generation,” Symmetry (Basel)., vol. 13, no. 2, pp. 1–26, Feb. 2021, doi: 10.3390/sym13020212.

Z. Hou, Y. Zhang, Q. Liu, and X. Ye, “A hybrid machine learning forecasting model for photovoltaic power,” Energy Reports, vol. 11, no. May, pp. 5125–5138, 2024, doi: 10.1016/j.egyr.2024.04.065.

S. Ghimire, R. C. Deo, D. Casillas-Pérez, S. Salcedo-Sanz, E. Sharma, and M. Ali, “Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction,” Meas. J. Int. Meas. Confed., vol. 202, no. April, p. 111759, 2022, doi: 10.1016/j.measurement.2022.111759.

H. Aprillia, H. T. Yang, and C. M. Huang, “Short-term photovoltaic power forecasting using a convolutional neural network-salp swarm algorithm,” Energies, vol. 13, no. 8, 2020, doi: 10.3390/en13081879.

U. Kumar, S. Mishra, and S. Madichetty, “An efficient SPV power forecasting using hybrid wavelet and genetic algorithm based LSTM deep learning model,” 2020 21st Natl. Power Syst. Conf. NPSC 2020, 2020, doi: 10.1109/NPSC49263.2020.9331910.

J. Jang, W. Jeong, S. Kim, B. Lee, M. Lee, and J. Moon, “RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values,” Sustain., vol. 15, no. 8, 2023, doi: 10.3390/su15086951.

W. Khan, S. Walker, and W. Zeiler, “Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach,” Energy, vol. 240, p. 122812, 2022, doi: 10.1016/j.energy.2021.122812.

H. Alghamdi et al., “Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System,” Int. J. Energy Res., vol. 2023, no. Ml, 2023, doi: 10.1155/2023/6418897.

Q. Li, X. Zhang, T. Ma, C. Jiao, H. Wang, and W. Hu, “A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine,” Energy, vol. 224, p. 120094, 2021, doi: 10.1016/j.energy.2021.120094.

B. D. Dimd, S. Voller, U. Cali, and O. M. Midtgard, “A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context,” IEEE Access, vol. 10. Institute of Electrical and Electronics Engineers Inc., pp. 26404–26425, 2022. doi: 10.1109/ACCESS.2022.3156942.

M. Cikan and B. Kekezoglu, “Comparison of metaheuristic optimization techniques including Equilibrium optimizer algorithm in power distribution network reconfiguration,” Alexandria Eng. J., vol. 61, no. 2, pp. 991–1031, 2022, doi: 10.1016/j.aej.2021.06.079.

M. G. M. Abdolrasol et al., “Artificial neural networks based optimization techniques: A review,” Electron., vol. 10, no. 21, 2021, doi: 10.3390/electronics10212689.

DOI: https://doi.org/10.30591/jpit.v9i2.7051