Summary of Distributed Solar Generation Forecasting Using Attention-based Deep Neural Networks For Cloud Movement Prediction, by Maneesha Perera et al.
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
by Maneesha Perera, Julian De Hoog, Kasun Bandara, Saman Halgamuge
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel approach to forecasting distributed solar generation by leveraging deep neural networks with “attention” mechanisms that focus on important regions of cloud images. The researchers apply an attention-based convolutional long short-term memory network (ConvLSTM) and a self-attention-based method previously developed for video prediction to forecast cloud movement. They investigate the impact of these attention-based methods on forecasting distributed solar generation, comparing them to non-attention-based approaches. The results show that attention-based methods can improve solar forecast skill scores by 5.86% or more compared to non-attention-based methods, particularly for high-altitude clouds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict when the sun will shine on our rooftops! Right now, it’s hard to forecast because of all the clouds moving around. The researchers tried using special computer networks that pay attention to important parts of cloud pictures and found they work really well. They compared these new methods to old ones and showed that they can be up to 5.86% more accurate when predicting when the sun will shine on our homes. |
Keywords
» Artificial intelligence » Attention » Self attention
