Data in the experiment.

<div><p>This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neur...

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Bibliographic Details
Main Author:	Montaser Abdelsattar (20606102) (author)
Other Authors:	Mohamed A. Ismeil (19066118) (author), Karim Menoufi (20606105) (author), Ahmed AbdelMoety (20606108) (author), Ahmed Emad-Eldeen (20606111) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified mean absolute error including feature scaling convolutional neural network comprehensive comparative analysis artificial neural network advanced computational approaches study &# 8217 power output based enhancing data consistency predicting wind turbine improved model performance evaluating machine learning following ml models deep learning models among dl models best ml models machine learning deep learning study presents multivariate data dl models best performance assessed based wind speed wind direction xlink "> xgboost ), term memory rnn ), results show previous benchmarks preprocessing techniques parameter tuning optimizing hyperparameters novelty lies modeling non mae ), linear regression linear dependencies lightgbm ). et achieved environmental variables dl algorithms diverse range directly comparing cnn ), 000 observations
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Data in the experiment.

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