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

Abstract In this paper, our goal is to determine solar power generation utilising machine learning models based on weather data and AQI(Air Quality Index). This study benchmarks the performance of these models based on three novel methodologies to identify which yields the best accuracy in terms of R2 score and simultaneously results in the lowest Mean Absolute Error(MAE) and Root Mean Square Error(RMSE). Firstly, a time-series based approach is used to accurately capture the seasonality in this solar generation. Secondly, to address the excessive zeros in the solar generation data we switched to a zero-inflated model, which accounts for the cases where there is true zero solar generation(Night-time, winter, cloud cover etc). Finally, we applied Power Transform to formulate a more efficient 1 method of addressing the skewness in the data and to transform it into a more Gaussian-like distribution. This scaling method smoothed out the solar distribution data and gave a much better R2 score and lower overall MAE and RMSE scores. These findings demonstrate the overall success of our predictive models in accurately determining solar power generation. Since precise solar energy projections can help to maximise energy generation and utilisation, these findings have major implications for energy management and planning. A comprehensive framework for precise solar energy prediction based on AQI and weather features is provided by the combination of regular time series modeling, zero-inflated modeling, and power transformation techniques, which advances forecasting and decision-making for renewable energy sources.