Methodology based on data science for the development of a forecast of the ower generation of a photovoltaic solar plant
Main Article Content
Abstract
The use of photovoltaic solar plants for the generation of electrical energy has been constantly increasing in recent years, and many of these plants are connected to the external electrical network, which makes it necessary to forecast the electrical energy generated by the solar plants to assist in the management of the network operator. This research presents a methodology based on data science to develop the forecast of electrical energy generated from photovoltaic solar plants, using three different techniques for comparison purposes: time series analysis, multiple linear regression, and artificial neural network. Historical data of peak power, solar irradiance, ambient temperature, wind speed, and soiling rate from an experimental NREL photovoltaic solar plant were used. To evaluate the performance of the models, the RMSE, MAE, and MAPE metrics are used, resulting in the ARIMA model of the time series analysis having the best performance with a MAE of 1.38 kWh, RMSE of 1.40 kWh, and MAPE of 6.35 %. In the correlation analysis, it was determined that power generation was independent of the soiling rate, so this variable was discarded in the regression models.
Keywords
Machine Learning, solar irradiance, artificial neural network, linear regression, time series, ambient temperature Aprendizaje Automático, irradiancia solar, red neuronal artificial, regresión lineal, serie de tiempo, temperatura ambiente
References
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[2] A. Kumar Mittal, K. Mathur, and S. Mittal, “A review on forecasting the photovoltaic power using machine learning,” Journal of Physics: Conference Series, vol. 2286, no. 1, p. 012010, jul 2022. [Online]. Available: https://dx.doi.org/10.1088/17426596/2286/1/012010
[3] A.-N. Sharkawy, M. Ali, H. Mousa, A. Ali, and G. Abdel-Jaber, “Machine learning method for solar pv output power prediction,” SVU-International Journal of Engineering Sciences and Applications, vol. 3, no. 2, pp. 123–130, 2022. [Online]. Available: https: //doi.org/10.21608/svusrc.2022.157039.1066
[4] D. V. S. Krishna Rao Kasagani and P. Manickam, “Modeling of solar photovoltaic power using a two-stage forecasting system with operation and weather parameters,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 0, no. 0, pp. 1–19, 2022. [Online]. Available: https://doi.org/10.1080/15567036.2022.2032880
[5] D. Pattanaik, S. Mishra, G. P. Khuntia, R. Dash, and S. C. Swain, “An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network,” Open Engineering, vol. 10, no. 1, pp. 630–641, 2020. [Online]. Available: https://doi.org/10.1515/eng-2020-0073
[6] M. N. Akhter, S. Mekhilef, H. Mokhlis, and N. Mohamed Shah, “Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques,” IET Renewable Power Generation, vol. 13, no. 7, pp. 1009–1023, 2019. [Online]. Available: https://doi.org/10.1049/iet-rpg.2018.5649
[7] M. Alaraj, A. Kumar, I. Alsaidan, M. Rizwan, and M. Jamil, “Energy production forecasting from solar photovoltaic plants based on meteorological parameters for qassim region, Saudi Arabia,” IEEE Access, vol. 9, pp. 83 241–83 251, 2021. [Online]. Available:https://doi.org/10.1109/ACCESS.2021.3087345
[8] Anuradha, K., Erlapally, Deekshitha, Karuna, G., Srilakshmi, V., and Adilakshmi, K., “Analysis of solar power generation forecasting using machine learning techniques,” E3S Web Conf., vol. 309, p. 01163, 2021. [Online]. Available: https://doi.org/10.1051/e3sconf/202130901163
[9] M. Borunda, A. Ramirez, R. Garduno, G. Ruiz, S. Hernandez, and O. A. Jaramillo, “Photovoltaic power generation forecasting for regional assessment using machine learning,” Energies, vol. 15, no. 23, p. 8895, 2022. [Online]. Available: https://doi.org/10.3390/en15238895
[10] J. VanderPlas, Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc., 2016. [Online]. Available: https://bit.ly/3BkwSeM
[11] D. Cielen, A. Meysman, and M. Ali, Introducing Data Science: Big Data, Machine Learning, and more, using Python tools. Manning Publication, 2016. [Online]. Available: https://bit.ly/42wWD80
[12] DuraMAT. (2023) PVDAQ time-series with soiling signal - Data and Resources. Durable Module Materials Consortium. [Online]. Available: https://bit.ly/42NKc7t
[13] SolarDesignTool, Sanyo HIP200BA3 (200W) Solar Panel. SolarDesignTool, 2023. [Online]. Available: https://bit.ly/3pu1dFk
[14] W. McKinney, Python for Data AnalysisOreilly and Associate Series. "O’Reilly Media, Inc.", 2013. [Online]. Available: https://bit.ly/3HZnfGr
[15] A. Navlani, A. Fandango, and I. Idris, Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python. Packt Publishing Ltd, 2021. [Online]. Available: https://bit.ly/42voHsb
[16] B. Ratner, Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data. CRC Press, 2017. [Online]. Available: https://bit.ly/3VPx933
[17] I. A. Uribe, “Guía metodológica para la selección de técnicas de depuración de datos,” Master’s thesis, Universidad Nacional de Colombia, Medellín, Colombia, 2010. [Online]. Available: https://bit.ly/3VQ5n6t
[18] D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to Time Series Analysis and Forecasting. Wiley Series in Probability and Statistics, 2015. [Online]. Available: https://bit.ly/3LTZiRS
[19] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis. Pearson Education Limited, 2013. [Online]. Available: https://bit.ly/3LWEHMN
[20] V. Platas García, Contrastes de normalidad. Universidade de Santiago de Compostela. Facultade de Matemáticas, 2021. [Online]. Available: https://bit.ly/3MfxZ5Z
[21] A. Gulli, A. Kapoor, and S. Pal, Deep Learning with TensorFlow 2 and Keras. Packt Publishing, 2019. [Online]. Available: https://bit.ly/42MPT5r
[22] J. Moolayil, Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python. Apress, 2018. [Online]. Available: https://bit.ly/3nMtrL4
[23] F. Chollet, Deep Learning with Python. Manning Publications Company, 2017. [Online]. Available: https://bit.ly/3LV4a9w
[24] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control. Wiley Series in Probability and Statistics, 2008. [Online]. Available: https://bit.ly/44OEALU
[25] S. Makridakis, S. Wheelright, and R. Hyndman, Manual of Forecasting: Methods and Applications. Wiley-Interscience, 1998. [Online]. Available: http://dx.doi.org/10.13140/RG.2.1.2528.4880
[26] T. C. Mills, Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting. Elsevier, 2019. [Online]. Available: https://bit.ly/42sM5Xd
[27] D. N. Gujarati and D. C. Porter, Econometría. McGraw-Hill Interamericana, 2010. [Online]. Available: https://bit.ly/44Tq0mc
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Published
Nov 22, 2023
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Section
Electrical Engineering - Energies
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