Summary of Novel Application Of Relief Algorithm in Cascaded Artificial Neural Network to Predict Wind Speed For Wind Power Resource Assessment in India, by Hasmat Malik et al.
Novel application of Relief Algorithm in cascaded artificial neural network to predict wind speed for wind power resource assessment in India
by Hasmat Malik, Amit Kumar Yadav, Fausto Pedro García Márquez, Jesús María Pinar-Pérez
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study focuses on predicting wind speeds (WS) using artificial neural networks (ANNs), a reliable method for energy businesses and control of wind power generation. The authors demonstrate that ANNs outperform conventional models, but accuracy depends on input parameters and algorithm architecture. To select the most relevant inputs, they apply the Relief Algorithm (RA) to Indian sites, identifying atmospheric pressure, solar radiation, and relative humidity as key variables. A cascade ANN model is developed, achieving a root mean square error (RMSE) of 1.44 m/s for training and 1.49 m/s for testing data. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to predict wind speeds using special computers called artificial neural networks. They compared these computer models with others that are not as good. The results show that the artificial neural network models work better, but only if they get the right information from the start. To figure out what information is most important, the researchers used a special tool and found that things like air pressure, sunlight, and humidity help predict wind speeds well. They made a new model using this information and it was pretty accurate. |
Keywords
* Artificial intelligence * Neural network