Xgboost–sfs and double nested stacking ensemble model for photovoltaic power forecasting...

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[HTML][HTML] Short-term photovoltaic power forecasting based on a feature rise-dimensional two-layer ensemble learning model

H Wang, S Yan, D Ju, N Ma, J Fang, S Wang, H Li… - Sustainability, 2023 - mdpi.com
Photovoltaic (PV) power generation has brought about enormous economic and
environmental benefits, promoting sustainable development. However, due to the …
被引用次数：7 相关文章 所有 7 个版本

Artificial intelligence-based viscosity prediction of polyalphaolefin-boron nitride nanofluids

OA Alawi, HM Kamar, MM Shawkat… - International …, 2024 - inderscienceonline.com
Predicting viscosity's nanofluids can benefit all domains, including energy, thermofluids,
power systems, energy storage, materials, cooling, heating, and lubrication. The objective of …
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[PDF] researchgate.net

Load Demand Forecasting in Smart Microgrid Using NWP via Feature Engineering

M Mohamed, FE Mahmood, MA Abd… - … for Future Grids …, 2023 - ieeexplore.ieee.org
This study developed a machine learning model to forecast electrical demand for smart
micro-grids with the existing of numerical weather forecasting (NWP). The model uses three …
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PV Power Prediction Based on XGBoost Algorithm

Y Hong, J Yang, Z Yang, J Yan - 2023 IEEE 5th International …, 2023 - ieeexplore.ieee.org
With the rapid development of renewable energy, the promotion and deployment of
photovoltaic (PV) power stations are gradually advancing. However, the output of …
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Hybrid Whale Optimization based Bidirectional Gated Recurrent Unit with Pre-trained CNN model for Software Fault Detection

AK Bhardwaj, S Hs, PK Pareek… - 2023 International …, 2023 - ieeexplore.ieee.org
The deep learning model (DL) S-ResNet-152 (Squeeze-based ResNet-152) is used to pull
out the features. Then, a bidirectional gated auto network (Bi-GRU-AN) is used to predict …
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