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Smart modeling of photovoltaic energy production based on meteorological data and production capacity: Utilizing advanced machine learning algorithms | ||
| Biosystems Engineering and Renewable Energies | ||
| دوره 1، شماره 1، فروردین 2025، صفحه 70-74 اصل مقاله (362.89 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22069/bere.2025.23125.1014 | ||
| نویسندگان | ||
| Mohammad Hajian* ؛ Tayyeb Nazghelichi | ||
| Department of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| چکیده | ||
| This study investigates the prediction of photovoltaic (PV) energy production using advanced machine learning algorithms, leveraging meteorological data and production capacity from 300 residential PV plants in Sydney, Australia. The dataset was processed into daily values to account for weather variability, and three machine learning models, i.e., random forest regression (RFR), support vector regression (SVR), and light gradient boosting regression (LightGBR), were implemented. Following rigorous preprocessing and hyperparameter optimization, LightGBR exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.9020, a mean absolute error (MAE) of 3.1621, and a mean squared error (MSE) of 0.1005. Compared to previous studies, the optimized LightGBR model demonstrated enhanced accuracy in PV energy forecasting, underscoring its potential for improving predictive modeling in this domain. These findings have significant implications for optimizing energy distribution, enhancing smart grid integration, and supporting decision-making in energy management systems. Accurate forecasting of PV energy output is essential for improving operational efficiency, minimizing energy waste, and advancing sustainability objectives in renewable energy management. | ||
تازه های تحقیق | ||
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| کلیدواژه‌ها | ||
| Forecasting؛ Light gradient boosting regression؛ Machine learning؛ Photovoltaic systems؛ Random forest algorithm؛ Support vector regression | ||
| مراجع | ||
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Ahmed, S. I., Bhuiyan, K. A., Rahman, I., Salehfar, H., & Selvaraj, D. F. (2024). Reliability of regression based hybrid machine learning models for the prediction of solar photovoltaics power generation. Energy Reports, 12, 5009–5023. Alaraj, M., Kumar, A., Alsaidan, I., Rizwan, M., & Jamil, M. (2021). Energy Production Forecasting from Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia. IEEE Access, 9, 83241–83251. Bright, J. M., Killinger, S., & Engerer, N. A. (2019). Data article: Distributed PV power data for three cities in Australia. Journal of Renewable and Sustainable Energy, 11(3). British Petroleum. (2023). bp Energy Outlook 2023 edition 2023 explores the key trends and uncertainties. Statistical Review of World Energy, July, 1–53. Das, U. K., Tey, K. S., Seyedmahmoudian, M., Idna Idris, M. Y., Mekhilef, S., Horan, B., & Stojcevski, A. (2017). SVR-based model to forecast PV power generation under differentweather conditions. Energies, 10(7), 1–17. Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X., & Zeng, W. (2019). Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agricultural Water Management, 225, 105758. Giri, P., Paul, S., & Debnath, B. K. (2024). A fuzzy Graph Theory and Matrix Approach (fuzzy GTMA) to select the best renewable energy alternative in India. Applied Energy, 358, 122582. Jamil, I., Lucheng, H., Iqbal, S., Aurangzaib, M., Jamil, R., Kotb, H., Alkuhayli, A., & AboRas, K. M. (2023). Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models. Alexandria Engineering Journal, 76, 51–73. Karakurt, I., & Aydin, G. (2023). Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries. Energy, 263, 125650. Khezri, M., Karimi, M. S., Mamkhezri, J., Ghazal, R., & Blank, L. (2022). Assessing the impact of selected determinants on renewable energy sources in the electricity mix: The case of ASEAN countries. Energies, 15(13), 4604. Le, N. T., & Benjapolakul, W. (2019). Comparative electrical energy yield performance of micro-inverter pv systems using a machine learning approach based on a mixed-effect model of real datasets. IEEE Access, 7, 175126–175134. Mitchell, T. M. (1999). Machine learning and data mining. Communications of the ACM, 42(11), 30–36. Osman, M., & Qureshi, I. (2025). Review of photovoltaic and concentrated solar technologies including their performance, reliability, efficiency and storage. Results in Engineering, 25, 104424. Rao, Z., Yang, Z., Li, J., Li, L., & Wan, S. (2024). Prediction of photovoltaic power generation based on parallel bidirectional long short-term memory networks. Energy Reports, 12, 3620–3629. Sharma, V. (2022). A Study on Data Scaling Methods for Machine Learning. International Journal For Global Academic & Scientific Research (IJGASR), 1(1). Smith, P. F., Ganesh, S., & Liu, P. (2013). A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of Neuroscience Methods, 220(1), 85–91. Sulaiman, M. H., Jadin, M. S., Mustaffa, Z., Daniyal, H., & Mohd Azlan, M. N. (2024). Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning. Journal of Building Engineering, 94, 109948. Syauqi, A., Pavian Eldi, G., Andika, R., & Lim, H. (2024). Reducing data requirement for accurate photovoltaic power prediction using hybrid machine learning-physical model on diverse dataset. Solar Energy, 279, 112814. Tahir, M. F., Yousaf, M. Z., Tzes, A., El Moursi, M. S., & El-Fouly, T. H. M. (2024). Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization. Renewable and Sustainable Energy Reviews, 200, 114581. Visual Crossing. (n.d.). Weather Query Builder - Sydney. Retrieved March 11, 2025, from https://www.visualcrossing.com/weather-query-builder/sydney/?v=wizard Wan, M. J., Phuang, Z. X., Hoy, Z. X., Dahlan, N. Y., Azmi, A. M., & Woon, K. S. (2024). Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment. Science of the Total Environment, 912, 168779. Wang, J., Ye, L., Ding, X., & Dang, Y. (2024). A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation. Energy, 304, 131939. Zhang, R., Bu, S., Zhou, M., Li, G., Zhan, B., & Zhang, Z. (2024). Deep reinforcement learning based interpretable photovoltaic power prediction framework. Sustainable Energy Technologies and Assessments, 67, 103830. Zhao, Y., Peng, X., Tu, T., Li, Z., Yan, P., & Li, C. (2024). WOA-VMD-SCINet: Hybrid model for accurate prediction of ultra-short-term Photovoltaic generation power considering seasonal variations. Energy Reports, 12, 3470–3487. | ||
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