Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features
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 o...
| Published in: | Discover Sustainability |
|---|---|
| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2024
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213685937&doi=10.1007%2fs43621-024-00783-5&partnerID=40&md5=f45c8c868b50c1efa158f9dcdfef51ae |