Comparative Analysis of MLP and CNN-LSTM Models for Solar Power Generation Forecasting-Reference-Cited by-同舟云学术

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Comparative Analysis of MLP and CNN-LSTM Models for Solar Power Generation Forecasting

Published:2023-10-17 Issue: Volume: Page:284-289
ISSN:
Container-title:2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)
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Author:

Jannah Nurul¹,Hanifah Mohd Shahrin Abu²,Gunawan Teddy Surya¹,Zabidi Suriza Ahmad¹,Yusoff Siti Hajar¹,Sapihie Siti Nadiah Mohd³

Affiliation:

1. International Islamic University Malaysia,Electrical and Computer Engineering Dept.,Kuala Lumpur,Malaysia,53100

2. International Islamic University Malaysia,Information Systems Department,Kuala Lumpur,Malaysia,53100

3. National Petroleum Limited,Petronas Research Sdn Bhd,Bandar Baru Bangi,Malaysia,43000

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10373396/10373418/10373538.pdf?arnumber=10373538

Reference10 articles.

1. Renewable Capacity Statistics 2020;International Renewable Energy Agency,2020

2. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods

3. One-hour-ahead solar radiation forecasting by MLP, LSTM, and ANFIS approaches

4. Solar Power Forecasting Using CNN-LSTM Hybrid Model

5. Hourly Photovoltaic Power Forecasting Using CNN-LSTM Hybrid Model

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel Solar PV Generation Forecasting Model for Standalone Power System Using Artificial Neural Network Controller;2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST);2024-04-11

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