Long-Term Prediction of Solar Radiation Using XGboost, LSTM, and Machine Learning Algorithms-Reference-Cited by-同舟云学术

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Long-Term Prediction of Solar Radiation Using XGboost, LSTM, and Machine Learning Algorithms

Published:2022-03-25 Issue: Volume: Page:
ISSN:
Container-title:2022 4th Asia Energy and Electrical Engineering Symposium (AEEES)
language:
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Author:

Bamisile Olusola¹,Ejiyi Chukwuebuka J.²,Osei-Mensah Emmanuel³,Chikwendu Ijeoma A.³,Li Jian⁴,Huang Qi¹

Affiliation:

1. College of Nuclear Science and Technology, Chengdu University of Technology,Chengdu,China

2. School of Information and Software Engineering, University of Electronic Science and Technology of China,Chengdu,China

3. School of Information and Communication Engineering, University of Electronic Science and Technology of China,Chengdu,China

4. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,Chengdu,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9759537/9759400/09759719.pdf?arnumber=9759719

Reference12 articles.

1. Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

2. Different states of multi-block based forecast engine for price and load prediction

3. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis

4. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

5. Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant

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4. Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines;Energies;2023-11-28

5. Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm;Energy Reports;2023-11

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