Multi-Step Solar Power Forecasting using Deep Learning Methods-Reference-Cited by-同舟云学术

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Multi-Step Solar Power Forecasting using Deep Learning Methods

Published:2022-11-26 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE)
language:
Short-container-title:

Author:

Nahed Zemouri¹,Hassen Bouzgou²,Chouder Aissa³,Mohamed Douak⁴

Affiliation:

1. University of Mohamed Boudiaf M’Sila,Department of Electronics

2. University of Batna2,Department of Industrial Engineering

3. University of Mohamed Boudiaf M’Sila,Electrical Engineering Laboratory (LGE)

4. University of Kasdi Merbah,Department of Renewable Energy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10093233/10093088/10093688.pdf?arnumber=10093688

Reference12 articles.

1. Long Short-Term Memory

2. Prospects of hydrogen production potential from renewable resources in Algeria;soumia;International Journal of Hydrogen Energy,2017

3. A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects

4. Recent advances in convolutional neural networks

5. Photovoltaic power forecasting using statistical methods: impact of weather data

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