Summary of A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model For Variable Terrain Conditions, by Sourav Malakar et al.
A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model for Variable Terrain Conditions
by Sourav Malakar, Saptarsi Goswami, Amlan Chakrabarti, Bhaswati Ganguli
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 The novel adaptive model for short-term wind speed (WS) forecasting in complex terrain is presented, utilizing Partial Auto Correlation Function (PACF) to minimize the dimension of Intrinsic Mode Functions (IMFs), reducing training time. The sample entropy (SampEn) is used to calculate the complexity of reduced IMFs, allowing for adaptive selection of Deep Learning (DL) model-features. A bidirectional feature-LSTM framework is suggested for complicated IMFs, resulting in improved forecasting accuracy. Compared to persistence, hybrid, Ensemble empirical mode decomposition (EEMD), and Variational Mode Decomposition (VMD)-based deep learning models, the proposed model shows superior forecasting performance, achieving a low variance of 0.70% between simple and complex terrain conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists create a new way to predict wind speed in areas with hills, mountains, or valleys. These places have unpredictable wind patterns that can make it hard to forecast wind speeds accurately. The researchers use a special method called Partial Auto Correlation Function (PACF) to reduce the amount of data they need to work with, making their model faster and more accurate. They also use a technique called sample entropy to determine which parts of the data are most important. By combining these approaches, they create an adaptive model that can predict wind speeds better than other methods. |
Keywords
» Artificial intelligence » Deep learning » Lstm
