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

• Published in: DISCOVER SUSTAINABILITY
• Main Authors: Khan, Sunawar; Mazhar, Tehseen; Khan, Muhammad Amir; Shahzad, Tariq; Ahmad, Wasim; Bibi, Afsha; Saeed, Mamoon M.; Hamam, Habib
• Format: Article
• Language: English
• Published: SPRINGERNATURE 2024
• Subjects: Science & Technology - Other Topics; Environmental Sciences & Ecology
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386434000001
• ISSN: 2662-9984
• DOI: 10.1007/s43621-024-00783-5

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.