EconPapers    
Economics at your fingertips  
 

Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

Narjes Azizi, Maryam Yaghoubirad, Meisam Farajollahi and Abolfzl Ahmadi

Renewable Energy, 2023, vol. 206, issue C, 135-147

Abstract: Solar radiation's intermittent and fluctuating nature poses severe limitations for most applications. Accurate prediction of solar radiation is an essential factor in predicting the output power of a photovoltaic power system. For this purpose, the potential of the 20 MW solar photovoltaic power plant in Zahedan city has been evaluated in this article. With the help of monthly data (1984–2021) and MLP, LSTM, GRU, CNN, and CNN-LSTM models, solar radiation and temperature are predicted for the next ten years. CNN exhibits the best performance compared to other models with four input parameters: global horizontal irradiance, temperature, surface pressure, relative humidity (RH), and two outputs of temperature and radiation. The root mean square error values for global horizontal irradiance and temperature were 12.68 W/m2 and 1.75 °C, respectively. Relative humidity exhibited more significant effect on the model in comparison with surface pressure. Finally, the average annual power output for ten years from 2022 to 2031 is calculated and predicted to be 50.37 GWh.

Keywords: Solar irradiance forecasting; Deep learning; Time series; Long-term prediction; Multi-step multivariate output (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148123001179
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:206:y:2023:i:c:p:135-147

DOI: 10.1016/j.renene.2023.01.102

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:135-147