Summary of Predicting Solar Energy Generation with Machine Learning Based on Aqi and Weather Features, by Arjun Shah et al.
Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features
by Arjun Shah, Varun Viswanath, Kashish Gandhi, Nilesh Madhukar Patil
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an accurate solar energy prediction model by exploring the influence of air quality index (AQI) and weather features on solar energy generation. Advanced machine learning and deep learning techniques are employed, including time series modeling, power transform normalization, and zero-inflated modeling. Various machine learning algorithms and a Conv2D Long Short-Term Memory model-based deep learning architecture are applied to predict solar energy generation with enhanced accuracy. The results demonstrate the effectiveness of the approach, achieving an R^2 score of 0.9691, mean absolute error (MAE) of 0.18, and root mean squared error (RMSE) of 0.10 using the Conv2D Long Short-Term Memory model. This innovation in time series forecasting contributes valuable insights to the synergy between AQI, weather features, and deep learning techniques for solar energy prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a more accurate way to predict solar energy generation by considering air quality index (AQI) and weather conditions. It uses special types of artificial intelligence called machine learning and deep learning. The researchers tried different approaches, including using information from the past about how much energy was being generated, making changes to the data to make it easier to work with, and combining different AI techniques. The results show that this new approach is better than previous ones at predicting solar energy generation. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Mae » Time series
