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

TL;DR

This study develops a machine learning-based model incorporating AQI and weather data to accurately predict solar energy generation, utilizing novel normalization and deep learning techniques for improved forecasting.

Contribution

It introduces a novel combination of power transform normalization and zero-inflated modeling with deep learning for solar energy prediction using AQI and weather features.

Findings

Achieved high prediction accuracy with an R^2 score of 0.9691

Demonstrated the effectiveness of power transform normalization in time series forecasting

Conv2D LSTM model outperformed other models in accuracy

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 algorithms and 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 $R^2$ 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.