Improving Solar Power Forecasting Using Air Quality and Weather Data with Ensemble Learning
DOI:
https://doi.org/10.32628/IJSRST2613346Keywords:
Solar Power Prediction, Air Quality Index (AQI), Weather Forecasting, Machine Learning, Ensemble Models, Photovoltaic (PV) Systems, Renewable EnergyAbstract
Solar power generation is non-continuous and very much dependent on atmospheric conditions such as weather variability and pollution levels. Forecasting solar power accurately is important for effective energy planning, grid stability, and large-scale integration of PV systems. Traditional forecasting approaches (sensor-based) are effective, yet costly, depending on the location. This study proposes a data-driven solar power forecasting framework that integrates weather parameters and Air Quality Index (AQI) indicators using ensemble machine learning models. The study focuses on existing methodologies that use meteorological variables, air pollutant concentrations (PM2.5, PM10, NO2, SO2, CO2, O3) and solar irradiance indicators for solar power forecasting. This paper reviews machine learning approaches, including regression models, ensemble learning, and stacked architectures, aligning with prediction accuracy, scalability, and practical deployment. This paper also emphasizes the impact of integrating AQI parameters into solar prediction models and demonstrates how effectively the combined weather-AQI features enhance the accuracy of solar power predictions. This study suggests employing a stacked ensemble model based on tree-based learners to align with the trends identified in the literature. The review concludes by identifying key research gaps and future directions, highlighting the potential of AQI-aware, data-driven models for cost-effective, scalable, and reliable solar power forecasting in smart energy systems.
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Chuluunsaikhan, Tserenpurev. (2021). Predicting the Power Output of Solar Panels based on Weather and Air Pollution Features using Machine Learning. Journal of Korea Multimedia Society. 24. 222. 10.9717/kmms.2021.24.2.222.
Zhou, H., Liu, Q., Yan, K., Du, Y. (2021). Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT. Wireless Communications and Mobile Computing, 2021, Article ID 9249387. doi:10.1155/2021/9249387.
Ghosh, S., Dey, S., Ganguly, D., Roy, S. B., & Bali, K. (2022). Cleaner air would enhance India’s annual solar energy production by 6–28 TWh. Environmental Research Letters, 17(5), 054007. doi:10.1088/1748-9326/ac5d9a.
Galimova, T., Ram, M., & Breyer, C. (2022). Mitigation of air pollution and corresponding impacts during a global energy transition towards 100% renewable energy system by 2050. Energy Reports, 8, 14124-14143. ISSN 2352-4847. doi:10.1016/j.egyr.2022.10.343.
Jia, Dongyu & Yang, Liwei & Lv, Tao & Liu, Weiping & Gao, Xiaoqing & Zhou, Jiaxin. (2022). Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions. Renewable Energy. 187. 10.1016/j.renene.2022.02.002.
Jebli, I., Belouadha, F.-Z., Kabbaj, M. I., & Tilioua, A. (2021). Prediction of solar energy guided by Pearson correlation using machine learning. Energy, 224, 120109. doi:10.1016/j.energy.2021.120109
Liu, D., & Sun, K. (2019). Random forest solar power forecast based on classification optimization. Energy, 115940. doi:10.1016/j.energy.2019.115940
Sweerts, B., Pfenninger, S., Yang, S., Folini, D., van der Zwaan, B., & Wild, M. (2019). Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data. Nature Energy. doi:10.1038/s41560-019-0412-4
Zhou, L., Schwede, D. B., Appel, K. W., Mangiante, M. J., Wong, D. C., Napelenok, S. L., Whung, P.-Y., & Zhang, B. (2019). The impact of air pollutant deposition on solar energy system efficiency: an approach to estimate PV soiling effects with the Community Multiscale Air Quality (CMAQ) model. Science of the Total Environment, 651(Pt 1), 456–465. doi:10.1016/j.scitotenv.2018.09.194
Zazoum, B. (2022). Solar photovoltaic power prediction using different machine learning methods. Energy Reports, 8(Supplement 1), 19-25. ISSN 2352-4847. doi:10.1016/j.egyr.2021.11.183.
Lee, C.-H., Yang, H.-C., & Ye, G.-B. (2021). Predicting the Performance of Solar Power Generation Using Deep Learning Methods. Applied Sciences, 11(15), 6887. doi:10.3390/app11156887
Chiteka, K., Arora, R., Sridhara, S. N., & Enweremadu, C. C. (2020). A novel approach to Solar PV cleaning frequency optimization for soiling mitigation. Scientific African, 8, e00459. ISSN 2468-2276. doi:10.1016/j.sciaf.2020.e00459.
Kim, D.-W., Deo, R. C., Park, S.-J., Lee, J.-S., & Lee, W.-S. (2019). Weekly heat wave death prediction model using zero-inflated regression approach. Theoretical and Applied Climatology, 137, 823-838. doi: 10.1007/s00704-018-2632-4
Thomas, S. J. (2010). Model-based clustering for multivariate time series of counts. Rice University. ProQuest Dissertations Publishing. (Publication No. 3421317).
Yeom, J. M., Deo, R. C., Adamowski, J. F., Park, S., & Lee, C. S. (2020). Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea. Environmental Research Letters, 15(9), 094025. doi: 10.1088/1748-9326/ab9467
S. Wimalaratne, D. Haputhanthri, S. Kahawala, G. Gamage, D. Alahakoon and A. Jennings, ”UNISOLAR: An Open Dataset of Photovoltaic Solar Energy Generation in a Large Multi-Campus University Setting,” 2022 15th International Conference on Human System Interaction (HSI), 2022, pp. 1-5, doi: 10.1109/HSI55341.2022.986947
Feng, C.X. A comparison of zero-inflated and hurdle models for modeling zero-inflated count data. J Stat Distrib App 8, 8 (2021).
Shah, A., Viswanath, V., Gandhi, K., & Patil, N. M. (2024). Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features. Synapse, Computer Engineering, D.J. Sanghvi College of Engineering, Mumbai, India. arXiv:2408.12476 [cs.LG].
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