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

Autor: Khan, Sunawar, Mazhar, Tehseen, Khan, Muhammad Amir, Shahzad, Tariq, Ahmad, Wasim, Bibi, Afsha, Saeed, Mamoon M., Hamam, Habib
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Zdroj: Discover Sustainability; 12/31/2024, Vol. 5 Issue 1, p1-24, 24p
Abstrakt: 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 R2 score of 0.90, highlighting its proficiency in capturing intricate temporal relationships. GRU closely followed, demonstrating potential as a viable option due to its remarkable combination of computational efficiency and accuracy (MSE = 0.015, R2 = 0.88). In datasets abundant in spatial correlations, the CNN-LSTM hybrid demonstrated its utility by providing novel insights into spatial–temporal patterns; nonetheless, it lagged considerably in accuracy, with a mean squared error (MSE) of 0.020 and a R2 of 0.87. Conversely, traditional models demonstrated a reliable albeit less dynamic ability to elucidate the complexities of renewable energy data; for instance, Random Forest exhibited a mean squared error (MSE) of 0.025, while Support Vector Regression (SVR) recorded an MSE of 0.030. The results affirm that deep learning architectures, particularly LSTM, offer a transformative method for renewable energy forecasting, hence enhancing accuracy and reliability in energy management systems. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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