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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

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GrooveSquid.com Paper Summaries

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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
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