Summary of Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: a Case Study Of a Wind Farm in Saudi Arabia, by Yasmeen Aldossary and Nabil Hewahi and Abdulla Alasaadi
Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia
by Yasmeen Aldossary, Nabil Hewahi, Abdulla Alasaadi
First submitted to arxiv on: 17 Dec 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 This study proposes a novel wind speed forecasting (WSF) framework for stationary data based on a hybrid decomposition method and the Bidirectional Long Short-term Memory (BiLSTM). The framework combines Wavelet Packet Decomposition (WPD) and Seasonal Adjustment Method (SAM) to eliminate seasonal components and reduce forecasting complexity. The BiLSTM is then applied to forecast all deseasonalized decomposed subseries, using five years of hourly wind speed observations from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The proposed model outperforms 27 other models in single and multiple WSF, achieving an overall average mean absolute error of 0.176549, root mean square error of 0.247069, and R-squared error of 0.985987. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wind speed forecasting is important for power grids to ensure stability and controllability. This study proposes a new way to do this using a combination of two methods: Wavelet Packet Decomposition (WPD) and Seasonal Adjustment Method (SAM). The WPD helps remove seasonal patterns, making it easier to predict wind speeds. Then, the BiLSTM is used to forecast all the deseasonalized decomposed subseries. This new method is tested using real data from a wind farm in Saudi Arabia and shows that it’s better than many other methods. |
Keywords
» Artificial intelligence » Sam
