Summary of Wind Power Prediction Across Different Locations Using Deep Domain Adaptive Learning, by Md Saiful Islam Sajol et al.
Wind Power Prediction across Different Locations using Deep Domain Adaptive Learning
by Md Saiful Islam Sajol, Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman, Jubair Yusuf
First submitted to arxiv on: 18 May 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 A deep neural network (DNN) based domain adaptive approach is proposed for predicting wind power, which learns from spatially varying climatological data. By selecting effective weather features using a random forest approach and updating only the last few layers of the DNN model, the approach demonstrates higher accuracy compared to traditional non-adaptive methods, with improvements ranging from 6.14% to 28.44%. The proposed method can aid grid planners in forecasting available wind capacity and grid integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wind power prediction is crucial for a renewable energy source that’s intermittent. Different regions have different weather patterns, making it harder to predict. A new approach uses deep neural networks to learn from one region and adapt to another. It chooses the right weather features and updates only part of the network. This makes it faster and more accurate than usual methods, with improvements up to 28.44%. |
Keywords
» Artificial intelligence » Neural network » Random forest
