Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodology
2.1.1. PSO-XGBoost Model
2.1.2. Statistical Indicator
2.2. Model Construction
2.2.1. Data Description
2.2.2. Model Construction Based on the Data of Each Season
3. Results
3.1. Accuracy of the PV Power Prediction in Four Seasons
3.2. Stability of the PV Power Prediction in Four Seasons
3.3. Experimental Study of the PV Power Prediction Accuracy in Typical Days
4. Application of PSO-XGBoost Model
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
Title | Abbreviation |
PV | Photovoltaic |
PSO | Particle swarm optimization |
XGBoost | Extreme gradient boosting |
PSO-XGBoost | Extreme gradient boosting model based on particle swarm optimization |
PVPG | Photovoltaic power generation |
LMS | Least mean square |
RMSE | Root mean squared error |
ANN | Artificial neural network |
SVM | Support vector machine |
LS-SVM | Least squares support vector machine |
GBDT | Gradient boosting decision tree |
RF | Random forest |
R2 | Coefficient of determination |
MAE | Mean absolute error |
MPPT | Maximum power point tracking |
PI | Percentage increase |
NMIC | National meteorological information center |
CMA | China meteorological administration |
WT-PSO-SVM | Wavelet transform-particle swarm optimization-support vector machine |
References
- Li, D.; Zhu, D.; Ge, M.; Wu, S.; Wang, R.; Wang, B.; Wu, Y.; Yang, Y. Optimal configuration and field experiments for photovoltaic generation system of solar-powered hose-drawn traveler. Trans. ASABE 2019, 62, 1789–1801. [Google Scholar] [CrossRef]
- Graditi, G.; Ferlito, S.; Adinolfi, G. Comparison of photovoltaic plant power production prediction methods using a large measured dataset. Renew. Energy 2016, 90, 513–519. [Google Scholar] [CrossRef]
- Zhao, W.; Zhang, H.; Zheng, J.; Dai, Y.; Huang, L.; Shang, W.; Liang, Y. A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants. Energy 2021, 223, 120026. [Google Scholar] [CrossRef]
- Hu, K.; Cao, S.; Wang, L.; Li, W.; Lv, M. A new ultra-short-term photovoltaic power prediction model based on ground-based cloud images. J. Clean Prod. 2018, 200, 731–745. [Google Scholar] [CrossRef]
- Karchi, N.; Kulkarni, D.; Prado, R.P.d.; Divakarachari, P.B.; Patil, S.N.; Desai, V. Adaptive least mean square controller for power quality enhancement in solar photovoltaic system. Energies 2022, 15, 8909. [Google Scholar] [CrossRef]
- Das, S.R.; Hota, A.P.; Pandey, H.M.; Sahoo, B.M. Industrial power quality enhancement using fuzzy logic based photovoltaic integrated with three phase shunt hybrid active filter and adaptive controller. Appl. Soft. Comput. 2022, 121, 108762. [Google Scholar] [CrossRef]
- Houran, M.A.; Sabzevari, K.; Hassan, A.; Oubelaid, A.; V’eliz, M.T.; Khosravi, N. Active power filter module function to improve power quality conditions using GWO and PSO techniques for solar photovoltaic arrays and battery energy storage systems. J. Energy Storage 2023, 72, 108552. [Google Scholar] [CrossRef]
- Alia, A.; Norb, N.M.; Ibrahimc, T.; Romlied, M.F. Sizing and placement of solar photovoltaic plants by using time-series historical weather data. J. Renew. Sustain. Energy 2018, 10, 023702. [Google Scholar] [CrossRef]
- Mubarak, H.; Hammoudeh, A.; Ahmad, S.; Abdellatif, A.; Mekhilef, S.; Mokhlis, H.; Dupont, S. A hybrid machine learning method with explicit time encoding for improved Malaysian photovoltaic power prediction. J. Clean Prod. 2023, 382, 134979. [Google Scholar] [CrossRef]
- Yang, M.; Huang, X. Ultra-short-term prediction of photovoltaic power based on periodic extraction of PV energy and LSH algorithm. IEEE Access 2018, 6, 51200–51205. [Google Scholar] [CrossRef]
- Rana, M.; Koprinska, I. Neural network ensemble based approach for 2D-interval prediction of solar photovoltaic power. Energies 2016, 9, 829. [Google Scholar] [CrossRef]
- Alghamdi, H.; Maduabuchi, C.; Albaker, A.; Alatawi, I.; Alsenani, T.R.; Alsafran, A.S.; Almalaq, A.; AlAqil, M.; Abdelmohimen, M.A.H.; Alkhedher, M. A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods. Eng. Sci. Technol. 2023, 40, 101363. [Google Scholar] [CrossRef]
- Yadav, A.K.; Chandel, S.S. Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using artificial neural network and multiple linear regression models. Renew. Sustain. Energy Rev. 2017, 77, 955–969. [Google Scholar] [CrossRef]
- Al-Dahidi, S.; Ayadi, O.; Alrbal, M.; Adeeb, J. Ensemble approach of optimized artificial neural networks for solar photovoltaic power prediction. IEEE Access 2019, 7, 81741–81758. [Google Scholar] [CrossRef]
- Dong, X.; Shen, J.; Ma, Z.; He, Y.J. Simultaneous operating temperature and output power prediction method for photovoltaic modules. Energy 2022, 260, 124909. [Google Scholar] [CrossRef]
- Rosato, A.; Altilio, R.; Araneo, R.; Panella, M. Prediction in photovoltaic power by neural networks. Energies 2017, 10, 1003. [Google Scholar] [CrossRef]
- Mayer, M.-J.; Yang, D. Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting. Renew. Sust. Energ. Rev. 2023, 175, 113171. [Google Scholar] [CrossRef]
- Eseye, A.T.; Zhang, J.H.; Zheng, D.H. Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and meteorological information. Renew. Energy 2018, 118, 357–367. [Google Scholar] [CrossRef]
- Li, L.; Wen, S.; Tseng, M.; Wang, C. Renewable energy prediction: A novel short-term prediction model of photovoltaic output power. J. Clean Prod. 2019, 228, 359–375. [Google Scholar] [CrossRef]
- Malvoni, M.; Giorgi, M.G.D.; Congedo, P.M. Photovoltaic predict based on hybrid PCA-LSSVM using dimensionality reducted data. Neurocomputing 2016, 211, 72–83. [Google Scholar] [CrossRef]
- Kazem, H.A.; Yousif, J.H. Comparison of prediction methods of photovoltaic power system production using a measured dataset. Energy Convers. Manag. 2017, 148, 1070–1081. [Google Scholar] [CrossRef]
- Wang, J.; Ran, R.; Zhou, Y. A short-term photovoltaic power prediction model based on an FOS-ELM algorithm. Appl. Sci. 2017, 7, 423. [Google Scholar] [CrossRef]
- Zhu, R.; Guo, W.; Gong, X. Short-term photovoltaic power output prediction based on k-Fold cross-validation and an ensemble model. Energies 2019, 12, 1220. [Google Scholar] [CrossRef]
- Fan, J.; Yue, W.; Wu, L.; Zhang, F.; Cai, H.; Wang, X.; Lu, X.; Xiang, Y. Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric. For. Meteorol. 2018, 263, 225–241. [Google Scholar] [CrossRef]
- Feng, Y.; Cui, N.; Gong, D.; Zhang, Q.; Zhao, L. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric Water Manag. 2017, 193, 163–173. [Google Scholar] [CrossRef]
- Tan, K.; Wang, H.; Chen, L.; Du, Q.; Du, P.; Pan, C. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 2020, 382, 120987. [Google Scholar] [CrossRef]
- Fan, J.; Wang, X.; Wu, L.; Zhou, H.; Zhang, F.; Yu, X.; Lu, X.; Xiang, Y. Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Convers. Manag. 2018, 164, 102–111. [Google Scholar] [CrossRef]
- Feng, Y.; Cui, N.; Chen, Y.; Gong, D.; Hu, X. Development of data-driven models for prediction of daily global horizontal irradiance in Northwest China. J. Clean Prod. 2019, 223, 136–146. [Google Scholar] [CrossRef]
- Available online: https://data.cma.cn/dataService/cdcindex/datacode/A.0012.0001/show_value/normal.html (accessed on 6 January 2022).
- Pardo-Picazo, M.Á.; Juárez, J.M.; García-Márquez, D. Energy consumption optimization in irrigation networks supplied by a standalone direct pumping photovoltaic system. Sustainability 2018, 10, 4203. [Google Scholar] [CrossRef]
- Zhou, Y.; Zhou, N.; Gong, L.; Jiang, M. Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine. Energy 2020, 204, 117894. [Google Scholar] [CrossRef]
- Song, Z.; Cao, S.; Yang, H. Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches. Appl. Energy 2023, 339, 121005. [Google Scholar] [CrossRef]
- Lei, Z.; Guo, Z.; Tao, Q.; Xiong, Z.; Ye, J. XGBoost-based short-term prediction method for power system inertia and its interpretability. Energy Rep. 2023, 9, 1458–1469. [Google Scholar] [CrossRef]
Key Parameters | Default Value | Range |
---|---|---|
max_depth | 6 | [0, ∞) |
min_child_weight | 1 | [0, ∞) |
learning_rate | 0.3 | [0, 1] |
gamma | 1 | [0, ∞) |
subsample | 1 | (0, 1] |
colsample_bytree | 1 | (0, 1] |
Key Parameters | Spring | Summer | Autumn | Winter | ||||
---|---|---|---|---|---|---|---|---|
XGBoost | PSO-XGBoost | XGBoost | PSO-XGBoost | XGBoost | PSO-XGBoost | XGBoost | PSO-XGBoost | |
max_depth | 5 | 11 | 5 | 10 | 4 | 10 | 4 | 12 |
min_child_weight | 9 | 8 | 7 | 6 | 5 | 7 | 9 | 8 |
learning_rate | 0.35 | 0.38 | 0.43 | 0.45 | 0.40 | 0.42 | 0.34 | 0.31 |
gamma | 0.54 | 0.65 | 0.62 | 0.73 | 0.61 | 0.78 | 0.52 | 0.61 |
subsample | 0.61 | 0.42 | 0.52 | 0.35 | 0.64 | 0.44 | 0.77 | 0.27 |
colsample_bytree | 0.74 | 0.41 | 0.63 | 0.56 | 0.78 | 0.49 | 0.81 | 0.22 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, D.; Zhu, D.; Tao, T.; Qu, J. Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models. Processes 2024, 12, 39. https://doi.org/10.3390/pr12010039
Li D, Zhu D, Tao T, Qu J. Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models. Processes. 2024; 12(1):39. https://doi.org/10.3390/pr12010039
Chicago/Turabian StyleLi, Dan, Delan Zhu, Tao Tao, and Jiwei Qu. 2024. "Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models" Processes 12, no. 1: 39. https://doi.org/10.3390/pr12010039
APA StyleLi, D., Zhu, D., Tao, T., & Qu, J. (2024). Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models. Processes, 12(1), 39. https://doi.org/10.3390/pr12010039