The Impact of Meteorological Data on the Accuracy of Solar Electricity Generation Forecasting Using Neural Networks

Yuriy Sayenko (), Ryszard Pawelek, Tetiana Baranenko and Vadym Liubartsev
Additional contact information
Yuriy Sayenko: Institute of Electrical Power Engineering, Lodz University of Technology, 20 Stefanowskiego Street, 90-537 Lodz, Poland
Ryszard Pawelek: Institute of Electrical Power Engineering, Lodz University of Technology, 20 Stefanowskiego Street, 90-537 Lodz, Poland
Tetiana Baranenko: Department of Automation Electrical Systems and Electric Drive, Pryazovskyi State Technical University, 19 Dmytro Yavornytskyi Avenue, 49005 Dnipro, Ukraine
Vadym Liubartsev: Department of Automation Electrical Systems and Electric Drive, Pryazovskyi State Technical University, 19 Dmytro Yavornytskyi Avenue, 49005 Dnipro, Ukraine

Energies, 2025, vol. 18, issue 9, 1-19

Abstract: The resolution of key tasks related to energy resource conservation and the enhancement of energy security is not possible without the widespread use of the electricity generated by renewable energy sources (RES), including, among others, photovoltaic (solar) power plants. A negative aspect of connecting renewable energy sources to power grids is the challenge of forecasting their generation, which consequently makes it difficult to plan the stable operation of power systems with distributed generation. To improve the accuracy of forecasting electricity generation by solar power plants, this article proposes the use, in addition to meteorological parameters such as air temperature and wind speed, of a new indicator defined as the unit active power generation ( P* ). This indicator can be used in the development of mathematical models and methods to increase the energy efficiency of power systems utilizing renewable energy sources, particularly to enhance the accuracy of power generation forecasting processes using neural networks and machine learning. The article shows that the use of this indicator allows for increasing the accuracy of forecasting energy production by solar power plants.

Keywords: solar power plants; meteorology; solar radiation intensity; forecasting; correlation; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/9/2309/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/9/2309/ (text/html)

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:gam:jeners:v:18:y:2025:i:9:p:2309-:d:1647371

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().