Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants

Muhammed A. Hassan, Loiy Al-Ghussain, Adel Khalil and Sayed A. Kaseb

Renewable Energy, 2022, vol. 188, issue C, 1120-1140

Abstract: This study presents two novel ensemble forecasters that combine time delay and recurrent neural networks to predict the global horizontal irradiance (GHI), direct normal irradiance (DNI), and ambient temperature (TMP). The weights of these forecasters are optimized to minimize the summation of squared errors between the actual and the forecasted values of sub-models during the previous forecasting step. The developed models have been benchmarked against typical forecasting models for three forecasting horizons. Afterward, the impact of forecasting errors on the power estimation of photovoltaic (PV) and concentrated solar power (CSP) plants was examined. The two proposed forecasters consistently outperformed persistence ones by up to 88.653%. Compared to the aggregated model, the newly proposed forecasters enhanced the predictions of GHI, DNI, and TMP by up to 39.392, 27.081, and 79.598% in terms of mean absolute error, and up to 21.501, 13.645, and 78.961% in terms of root mean square error. The absolute and relative performances of the proposed forecasters also improved as the forecasting horizon decreased. Finally, the performances of the daily and the instantaneously calibrated models were comparable in both plants. Hence, the daily calibrated model is generally recommended as it requires significantly lower computational costs.

Keywords: Solar irradiance; Hybrid forecasting model; Auto calibrated weights; Time-delay neural network; NARX; Power generation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122002476
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:188:y:2022:i:c:p:1120-1140

DOI: 10.1016/j.renene.2022.02.098

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 ().