Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

Abstract This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, and SVR—aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting. LSTM achieved a remarkable Mean Squared Error (MSE) of 0.010 and R...

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Bibliographic Details
Main Authors: Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam
Format: Article
Language:English
Published: Springer 2024-12-01
Series:Discover Sustainability
Subjects:
Renewable energy
Forecasting
Deep learning
LSTM
Neural networks
Hyperparameter tuning
Online Access:https://doi.org/10.1007/s43621-024-00783-5
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https://doi.org/10.1007/s43621-024-00783-5

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