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 Authors: | , , , , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
SPRINGERNATURE
2024
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| Subjects: | |
| Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386434000001 |