Deep learning approach for one-hour ahead forecasting of solar radiation in different climate regions-Reference-Cited by-同舟云学术

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Deep learning approach for one-hour ahead forecasting of solar radiation in different climate regions

Published:2024-04-18 Issue:13 Volume:21 Page:2984-3000
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Yildirim Alper¹,Bilgili Mehmet²^ORCID,Kara Osman³

Affiliation:

1. Department of Machinery and Metal Technology, Osmaniye Vocational School of Higher Education, Osmaniye Korkut Ata University, Osmaniye, Turkey

2. Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, Adana, Turkey

3. Department of Energy Engineering, Engineering Faculty, Osmaniye Korkut Ata University, Osmaniye, Turkey

Funder

no funding

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2024.2341824

Reference43 articles.

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

2. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends

3. Comparative analysis of data‐driven methods for daily reference evapotranspiration estimation of Southern Caspian Sea

4. Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

5. Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches

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