A Review Paper on Solar Power Prediction Using AQI And Weather Forecasting

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 review paper presents a comparative analysis of software-based solar power prediction using weather forecast and AQI data, which replaces the traditional sensor-based techniques. 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.