Improving Solar Power Forecasting Using Air Quality and Weather Data with Ensemble Learning

Apurva Kulkarni, Prem Suryawanshi, Garima Agrawal, Nidhi Agrawal and Faizan Khandwani

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 670-679

Abstract: 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.

Keywords: Solar Power Prediction; Air Quality Index (AQI); Weather Forecasting; Machine Learning; Ensemble Models; Photovoltaic (PV) Systems; Renewable Energy (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST2613346 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST2613346/IJSRST2613346 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1502

DOI: 10.32628/IJSRST2613346

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().