An Ensemble Approach for Intra-Hour Forecasting of Solar Resource

Sergiu-Mihai Hategan, Nicoleta Stefu () and Marius Paulescu
Additional contact information
Sergiu-Mihai Hategan: Faculty of Physics, West University of Timisoara, V. Parvan 4, 300223 Timisoara, Romania
Nicoleta Stefu: Faculty of Physics, West University of Timisoara, V. Parvan 4, 300223 Timisoara, Romania
Marius Paulescu: Faculty of Physics, West University of Timisoara, V. Parvan 4, 300223 Timisoara, Romania

Energies, 2023, vol. 16, issue 18, 1-12

Abstract: Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky imagery, is proposed. This study is conducted with high-quality data and high-resolution sky images recorded on the Solar Platform of the West University of Timisoara, Romania. Atmospheric factors that contribute to improving or reducing the quality of forecasts are discussed. Generally, the statistical models gain a small skill score across all forecast horizons (5 to 30 min). The machine-learning-based methods perform best at smaller forecast horizons (less than 15 min), while the all-sky-imagery-based model performs best at larger forecast horizons. Overall, for forecast horizons between 10 and 30 min, the weighted forecast ensemble with frozen coefficients achieves a skill score between 15 and 20%.

Keywords: solar irradiance; nowcasting; machine learning; sky imager; ensemble forecast (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/18/6608/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/18/6608/ (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:16:y:2023:i:18:p:6608-:d:1239431

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 ().