Summary of Location Agnostic Source-free Domain Adaptive Learning to Predict Solar Power Generation, by Md Shazid Islam et al.
Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation
by Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman, Jubair Yusuf, Md Saiful Islam Sajol, Farhana Akter Tumpa
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a domain adaptive deep learning-based framework to estimate solar power generation using weather features, addressing the challenges of spatial and temporal variability in climatic characteristics. The model is trained on a known location dataset in a supervised manner and then used to predict solar power for an unknown location. This approach shows advantages in computing speed, storage efficiency, and improved outcomes in scenarios where non-adaptive methods fail. The framework demonstrates an improvement of 10.47%, 7.44%, and 5.11% in solar power prediction accuracy compared to the best performing non-adaptive method for California, Florida, and New York, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us predict how much energy we can get from the sun at different places and times. This is important because the weather changes over time and place, which makes it hard to make accurate predictions. The authors suggest a new way of using deep learning computers to improve these predictions. They train their model on data from one place and then use it to predict energy output in another place. This approach is faster and uses less storage than other methods, and it even works better when the weather patterns change. |
Keywords
* Artificial intelligence * Deep learning * Supervised
