Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features

Aug 4, 2026

Accurate solar energy prediction supports reliable grid integration and efficient resource planning. This study models the impact of Air Quality Index (AQI) alongside meteorological features using advanced Machine Learning and Deep Learning methods. A time-series forecasting pipeline combines power-transform normalization with zero-inflated modeling to address irregular solar generation patterns. Multiple algorithms, including a Conv2D Long Short-Term Memory architecture, are evaluated after these transformations, achieving strong predictive performance with R2, MAE, and RMSE results that validate the added value of AQI and weather variables.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

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Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features Arjun Shah Synapse, Computer Engineering D.J. Sanghvi College of Engineering Mumbai, India [arjun.a.shah244@gmail.com](arjun.a.shah244@gmail.com) Varun Viswanath Synapse, Computer Engineering D.J. Sanghvi College of Engineering Mumbai, India [varunvis2903@gmail.com](varunvis2903@gmail.com) Kashish Gandhi Synapse, Computer Engineering D.J. Sanghvi College of Engineering Mumbai, India [kashishgandhi6112003@gmail.com](kashishgandhi6112003@gmail.com) arXiv :2408 . 12476v 3 [ cs .LG] 3 Oct 2024 Dr. Nilesh Madhukar Patil Synapse, Computer Engineering D.J. Sanghvi College of Engineering Mumbai, India [nilesh.p@djsce.ac.in](nilesh.p@djsce.ac.in) Abstract—This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine Learning and Deep Learning techniques. Our methodology uses time series modeling and makes novel use of power transform normalization and zero-inflated modeling. Various Machine Learning algorithmsand Conv2D Long Short-Term Memory model based Deep Learning models are applied to these transformations for precise predictions. Results underscore the effectiveness of our approach, demonstrating enhanced prediction accuracy with Air Quality Index and weather features. We achieved a 0.9691 R2 Score, 0.18 MAE, 0.10 RMSE with Conv2D Long Short-Term Memory model, showcasing the power transform technique’s innovation in enhancing time series forecasting for solar energy generation. Such results help our research contribute valuable insights to the synergy between Air Quality Index, weather features, and Deep Learning techniques for solar energy prediction. Index Terms—Solar Power Generation, Zero Inflated Model, Power Transform, Time series, LSTM, Deep Learning I. INTRODUCTION In the modern world, it has become increasingly clear that eliminating fossil fuels is one of the huge requirements to achieve a carbon-neutral future. The Working Group III Special Report on Renewable Energy Sources and Climate Change Mitigation (SRREN) [19] suggests that consumption of fossil fuels accounts for the majority of anthropogenic GHG emissions worldwide. It states that CO2 consumption had risen to over 390 ppm, which was around 39% above preindustrial levels by the end of 2010 . In the race to an efficient energy ecosystem, solar energy is a promising renewable resource, but its intermittent nature poses challenges for integration into the grid. A recent study shows that India lost 29% of its utilizable global horizontal irradiance potential due to air pollution in the period between 2008 and 2018 [3] . Effective prediction of solar power generation is crucial for efficient planning and management of solar resources. Renewable energy like solar power is said to benefit human beings in a lot of different ways and the most important is in the health domain. Research by Galimova et al. [4] suggests that by 2050 if the world goes under a global transition and the energy sector emissions drop by 92% then we can reduce premature deaths by air pollution by 97% . This study reinforced the significance of considering environmental factors in our solar energy forecasting models. This study examines the use of machine learning algorithms that incorporate Air Quality Index (AQI) and meteorological features to improve forecast accuracy. In order to shed some light on the inconsistent patterns of solar generation data, a number of regression models were initially utilised to predict the per-hour generation of solar power. We thus benchmarked a number of regression models, of which the chief ones were Linear Regression, Lasso, Ridge, ElasticNet, and ensemble models like RandomForest and XGBoost. These models utilize different methodologies which w

FAQs

Why is predicting solar energy generation important for power systems?

Accurate forecasts enable efficient planning and management of solar resources and support grid integration despite solar intermittency.

Which factors are incorporated to improve prediction accuracy?

The approach uses the Air Quality Index (AQI) and meteorological (weather) features to explain fluctuations in solar generation data.

What modeling techniques help handle irregular solar generation patterns?

The pipeline applies power transform normalization and zero-inflated modeling, and it uses time-series modeling with Conv2D LSTM-based deep learning models for spatiotemporal dependence.

Aug 4, 2026

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