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

Arjun Shah, Varun Viswanath, Kashish Gandhi, Nilesh Madhukar Patil

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.

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

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 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.
Paper Structure (10 sections, 3 equations, 10 figures, 5 tables)

This paper contains 10 sections, 3 equations, 10 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Survey of Literature
  3. Methodology
  4. Dataset
  5. Time-Series Approach
  6. Zero-Inflated Models
  7. Power-Transform
  8. Results
  9. Conclusion
  10. Future Scope

Figures (10)

  • Figure 1: Location where dataset was sourced from (La Trobe University) along with nearest AQI station (Macleod, Victoria)
  • Figure 2: Distance in kilometers between La Trobe University and Macleod AQI station (1.77km)
  • Figure 4: Histogram of the initial solar power distribution before application of transformations, highlighting the zero-inflated nature of our dataset.
  • Figure 5: A plot demonstrating a standard Tweedie distribution.
  • Figure 6: Histogram of solar power distribution after applying power transform which is analogous to the tweedie distribution in figure 5.
  • ...and 5 more figures