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...
| الحاوية / القاعدة: | DISCOVER SUSTAINABILITY |
|---|---|
| المؤلفون الرئيسيون: | , , , , , , , , |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
SPRINGERNATURE
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386434000001 |