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

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

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.

Suggested Citation

  • Hassan, Muhammed A. & Al-Ghussain, Loiy & Khalil, Adel & Kaseb, Sayed A., 2022. "Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants," Renewable Energy, Elsevier, vol. 188(C), pages 1120-1140.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:1120-1140
    DOI: 10.1016/j.renene.2022.02.098
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    References listed on IDEAS

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    1. Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    2. Kaood, Amr & Abubakr, Mohamed & Al-Oran, Otabeh & Hassan, Muhammed A., 2021. "Performance analysis and particle swarm optimization of molten salt-based nanofluids in parabolic trough concentrators," Renewable Energy, Elsevier, vol. 177(C), pages 1045-1062.
    3. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    4. Bailek, Nadjem & Bouchouicha, Kada & Hassan, Muhammed A. & Slimani, Abdeldjalil & Jamil, Basharat, 2020. "Implicit regression-based correlations to predict the back temperature of PV modules in the arid region of south Algeria," Renewable Energy, Elsevier, vol. 156(C), pages 57-67.
    5. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2015. "A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance," Energy, Elsevier, vol. 82(C), pages 570-577.
    6. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    8. Al-Ghussain, Loiy & Abubaker, Ahmad M. & Darwish Ahmad, Adnan, 2021. "Superposition of Renewable-Energy Supply from Multiple Sites Maximizes Demand-Matching: Towards 100% Renewable Grids in 2050," Applied Energy, Elsevier, vol. 284(C).
    9. Hay, John E., 1993. "Calculating solar radiation for inclined surfaces: Practical approaches," Renewable Energy, Elsevier, vol. 3(4), pages 373-380.
    10. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo, 2008. "The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany," Energy Policy, Elsevier, vol. 36(8), pages 3076-3084, August.
    11. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    12. Eduardo Rangel & Erasmo Cadenas & Rafael Campos-Amezcua & Jorge L. Tena, 2020. "Enhanced Prediction of Solar Radiation Using NARX Models with Corrected Input Vectors," Energies, MDPI, vol. 13(10), pages 1-22, May.
    13. Prasad, Ramendra & Ali, Mumtaz & Kwan, Paul & Khan, Huma, 2019. "Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation," Applied Energy, Elsevier, vol. 236(C), pages 778-792.
    14. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    15. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.
    16. Hassan, Muhammed A. & Khalil, Adel & Abubakr, Mohamed, 2021. "Selection methodology of representative meteorological days for assessment of renewable energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 34-51.
    17. Li, Shuai & Ma, Hongjie & Li, Weiyi, 2017. "Typical solar radiation year construction using k-means clustering and discrete-time Markov chain," Applied Energy, Elsevier, vol. 205(C), pages 720-731.
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    3. Hassan, Muhammed A. & Fouad, Aya & Dessoki, Khaled & Al-Ghussain, Loiy & Hamed, Ahmed, 2023. "Performance analyses of supercritical carbon dioxide-based parabolic trough collectors with double-glazed receivers," Renewable Energy, Elsevier, vol. 215(C).

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