Comparative Analysis of LSTM, GRU, and MLP Neural Networks for Short-Term Solar Power Forecasting-Reference-Cited by-同舟云学术

Comparative Analysis of LSTM, GRU, and MLP Neural Networks for Short-Term Solar Power Forecasting

Published:2023-11-05 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT)
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Author:

Boucetta Lakhdar Nadjib¹,Amrane Youssouf¹,Arezki Saliha²

Affiliation:

1. University of Science and Technology,Faculty of Electrical Engineering,LSEI Laboratory, Departement of Electrical Engineering,Houari Boumediene Algiers,Algeria

2. University of Science and Technology,Faculty of Electrical Engineering,LSEI Laboratory, Department of Electrical Engineering,Houari Boumediene Algiers,Algeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424967/10425818/10426346.pdf?arnumber=10426346

Reference12 articles.

1. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network

2. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

3. Hybrid models for global solar radiation prediction: a case study

4. Long Short-Term Memory

5. Accurate photovoltaic power forecasting models using deep LSTM-RNN

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