IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics036054422403545x.html
   My bibliography  Save this article

A geographic multi-scale machine learning framework for predicting solar irradiation on tilted surfaces

Author

Listed:
  • Al-Dahidi, Sameer
  • Rinchi, Bilal
  • Dababseh, Raghad
  • Ayadi, Osama
  • Alrbai, Mohammad

Abstract

This study addresses the challenge of improving Global Tilted Irradiation (GTI) predictions, with Jordan serving as the case study. The novelty of the work lies in developing machine learning models that predict GTI at national, regional (3 regions), and city-specific (12 cities) levels, a previously unexplored approach in the literature. The research examines the comparative efficiency of using a single model for an entire country versus tailored models for individual regions and cities, shedding light on the trade-offs in model evaluation. Various regression models, including Neural Networks (NNs), Linear Regression (LR), Regression Trees (RTs), Ensemble of Regression Trees (ERTs), Support Vector Machine (SVM), and Kernel Approximation, were evaluated using performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). NNs consistently performed best, achieving the lowest RMSE (1.5787 kWh/m2) and highest R2 (99.8600 %) at the regional level. Sensitivity analysis further explored the impact of different time resolutions, revealing that monthly data outperformed daily data in terms of accuracy and computational efficiency. Ultimately, we conclude that region-specific models and monthly data resolution are optimal for practical GTI prediction.

Suggested Citation

  • Al-Dahidi, Sameer & Rinchi, Bilal & Dababseh, Raghad & Ayadi, Osama & Alrbai, Mohammad, 2024. "A geographic multi-scale machine learning framework for predicting solar irradiation on tilted surfaces," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s036054422403545x
    DOI: 10.1016/j.energy.2024.133767
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422403545X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133767?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Manni, Mattia & Jouttijärvi, Sami & Ranta, Samuli & Miettunen, Kati & Lobaccaro, Gabriele, 2024. "Validation of model chains for global tilted irradiance on East-West vertical bifacial photovoltaics at high latitudes," Renewable Energy, Elsevier, vol. 220(C).
    2. Hossam Fraihat & Amneh A. Almbaideen & Abdullah Al-Odienat & Bassam Al-Naami & Roberto De Fazio & Paolo Visconti, 2022. "Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan," Future Internet, MDPI, vol. 14(3), pages 1-24, March.
    3. Aliana, Arnau & Chang, Miguel & Østergaard, Poul Alberg & Victoria, Marta & Andersen, Anders N., 2022. "Performance assessment of using various solar radiation data in modelling large-scale solar thermal systems integrated in district heating networks," Renewable Energy, Elsevier, vol. 190(C), pages 699-712.
    4. Wiser, Ryan & Millstein, Dev & Mai, Trieu & Macknick, Jordan & Carpenter, Alberta & Cohen, Stuart & Cole, Wesley & Frew, Bethany & Heath, Garvin, 2016. "The environmental and public health benefits of achieving high penetrations of solar energy in the United States," Energy, Elsevier, vol. 113(C), pages 472-486.
    5. Zhu, Rui & Wong, Man Sing & You, Linlin & Santi, Paolo & Nichol, Janet & Ho, Hung Chak & Lu, Lin & Ratti, Carlo, 2020. "The effect of urban morphology on the solar capacity of three-dimensional cities," Renewable Energy, Elsevier, vol. 153(C), pages 1111-1126.
    6. Notton, Gilles & Paoli, Christophe & Vasileva, Siyana & Nivet, Marie Laure & Canaletti, Jean-Louis & Cristofari, Christian, 2012. "Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks," Energy, Elsevier, vol. 39(1), pages 166-179.
    7. Dahmani, Kahina & Dizene, Rabah & Notton, Gilles & Paoli, Christophe & Voyant, Cyril & Nivet, Marie Laure, 2014. "Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model," Energy, Elsevier, vol. 70(C), pages 374-381.
    8. Mehleri, E.D. & Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C., 2010. "A new neural network model for evaluating the performance of various hourly slope irradiation models: Implementation for the region of Athens," Renewable Energy, Elsevier, vol. 35(7), pages 1357-1362.
    9. Xu, Luting & Long, Enshen & Wei, Jincheng & Cheng, Zhu & Zheng, Hanjie, 2021. "A new approach to determine the optimum tilt angle and orientation of solar collectors in mountainous areas with high altitude," Energy, Elsevier, vol. 237(C).
    10. Hay, John E., 1993. "Calculating solar radiation for horizontal surfaces—II. Empirically based approaches," Renewable Energy, Elsevier, vol. 3(4), pages 365-372.
    11. Hay, John E., 1993. "Calculating solar radiation for inclined surfaces: Practical approaches," Renewable Energy, Elsevier, vol. 3(4), pages 373-380.
    12. Ramadhan, Raden A.A. & Heatubun, Yosca R.J. & Tan, Sek F. & Lee, Hyun-Jin, 2021. "Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power," Renewable Energy, Elsevier, vol. 178(C), pages 1006-1019.
    13. Hay, John E., 1993. "Calculating solar radiation for horizontal surfaces—I. Theoretically based approaches," Renewable Energy, Elsevier, vol. 3(4), pages 357-364.
    14. Torrente, Cristóbal J. & Reca, Juan & López-Luque, Rafael & Martínez, Juan & Casares, Francisco J., 2024. "Simulation model to analyze the spatial distribution of solar radiation in agrivoltaic Mediterranean greenhouses and its effect on crop water needs," Applied Energy, Elsevier, vol. 353(PA).
    15. Takilalte, Abdelatif & Harrouni, Samia & Yaiche, Mohamed Rédha & Mora-López, Llanos, 2020. "New approach to estimate 5-min global solar irradiation data on tilted planes from horizontal measurement," Renewable Energy, Elsevier, vol. 145(C), pages 2477-2488.
    16. Rivera, Nathaly M. & Ruiz-Tagle, J. Cristobal & Spiller, Elisheba, 2024. "The health benefits of solar power generation: Evidence from Chile," Journal of Environmental Economics and Management, Elsevier, vol. 126(C).
    17. Hsu-Yung Cheng & Chih-Chang Yu & Kuo-Chang Hsu & Chi-Chang Chan & Mei-Hui Tseng & Chih-Lung Lin, 2019. "Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles," Energies, MDPI, vol. 12(8), pages 1-14, April.
    18. Chih-Chiang Wei, 2017. "Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan," Energies, MDPI, vol. 10(10), pages 1-26, October.
    19. Shaddel, Mehdi & Javan, Dawood Seyed & Baghernia, Parisa, 2016. "Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 59-67.
    20. Notton, Gilles & Paoli, Christophe & Ivanova, Liliana & Vasileva, Siyana & Nivet, Marie Laure, 2013. "Neural network approach to estimate 10-min solar global irradiation values on tilted planes," Renewable Energy, Elsevier, vol. 50(C), pages 576-584.
    21. Wattan, Rungrat & Janjai, Serm, 2016. "An investigation of the performance of 14 models for estimating hourly diffuse irradiation on inclined surfaces at tropical sites," Renewable Energy, Elsevier, vol. 93(C), pages 667-674.
    22. Abo-Zahhad, Essam M. & Rashwan, Ahmed & Salameh, Tareq & Hamid, Abdul Kadir & Faragalla, Asmaa & El-Dein, Adel Z. & Chen, Yong & Abdelhameed, Esam H., 2024. "Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis," Renewable Energy, Elsevier, vol. 221(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jasiewicz Jarosław & Cierniewski Jerzy, 2021. "SALBEC – A Python Library and GUI Application to Calculate the Diurnal Variation of the Soil Albedo," Quaestiones Geographicae, Sciendo, vol. 40(3), pages 95-107, September.
    2. Kafka, Jennifer & Miller, Mark A., 2020. "The dual angle solar harvest (DASH) method: An alternative method for organizing large solar panel arrays that optimizes incident solar energy in conjunction with land use," Renewable Energy, Elsevier, vol. 155(C), pages 531-546.
    3. Manni, Mattia & Melkert, Tom & Lobaccaro, Gabriele & Jelle, Bjørn Petter, 2025. "Implementation and validation of virtual clones of coloured building-integrated photovoltaic facades," Applied Energy, Elsevier, vol. 378(PA).
    4. Hay, John E., 1993. "Solar radiation data: Validation and quality control," Renewable Energy, Elsevier, vol. 3(4), pages 349-355.
    5. Karen Barbosa de Melo & Hugo Soeiro Moreira & Marcelo Gradella Villalva, 2020. "Influence of Solar Position Calculation Methods Applied to Horizontal Single-Axis Solar Trackers on Energy Generation," Energies, MDPI, vol. 13(15), pages 1-15, July.
    6. Verena Weiler & Jonas Stave & Ursula Eicker, 2019. "Renewable Energy Generation Scenarios Using 3D Urban Modeling Tools—Methodology for Heat Pump and Co-Generation Systems with Case Study Application †," Energies, MDPI, vol. 12(3), pages 1-19, January.
    7. Chinchilla, Monica & Santos-Martín, David & Carpintero-Rentería, Miguel & Lemon, Scott, 2021. "Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data," Applied Energy, Elsevier, vol. 281(C).
    8. Hafez, A.Z. & Soliman, A. & El-Metwally, K.A. & Ismail, I.M., 2017. "Tilt and azimuth angles in solar energy applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 147-168.
    9. Rinchi, Bilal & Dababseh, Raghad & Jubran, Mayar & Al-Dahidi, Sameer & Abdalla, Mohammed E.B. & Ayadi, Osama, 2025. "Global prediction of optimal solar panel tilt angles via machine learning," Applied Energy, Elsevier, vol. 382(C).
    10. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    11. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    12. Dey, Sumon & Lakshmanan, Madan Kumar & Pesala, Bala, 2018. "Optimal solar tree design for increased flexibility in seasonal energy extraction," Renewable Energy, Elsevier, vol. 125(C), pages 1038-1048.
    13. Dahmani, Kahina & Notton, Gilles & Voyant, Cyril & Dizene, Rabah & Nivet, Marie Laure & Paoli, Christophe & Tamas, Wani, 2016. "Multilayer Perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements," Renewable Energy, Elsevier, vol. 90(C), pages 267-282.
    14. Zhenyu Wang & Cuixia Tian & Qibing Zhu & Min Huang, 2018. "Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition," Energies, MDPI, vol. 11(1), pages 1-21, January.
    15. Ghaffarpour, Zahra & Fakhroleslam, Mohammad & Amidpour, Majid, 2024. "Calculation of energy consumption, tomato yield, and electricity generation in a PV-integrated greenhouse with different solar panels configuration," Renewable Energy, Elsevier, vol. 229(C).
    16. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    17. Xue, Haiteng & Wang, Gongda & Li, Xijian & Du, Feng, 2024. "Predictive combination model for CH4 separation and CO2 sequestration with CO2 injection into coal seams: VMD-STA-BiLSTM-ELM hybrid neural network modeling," Energy, Elsevier, vol. 313(C).
    18. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    19. Riyad Mubarak & Martin Hofmann & Stefan Riechelmann & Gunther Seckmeyer, 2017. "Comparison of Modelled and Measured Tilted Solar Irradiance for Photovoltaic Applications," Energies, MDPI, vol. 10(11), pages 1-18, October.
    20. Yin, Kaili & Zhang, Xiaojing & Cheng, Xiangyang & Xie, Jingchao & Liu, Jiaping, 2024. "Estimation of total solar irradiance on vertical surfaces for all-sky conditions adaptive to coastland surfaces," Energy, Elsevier, vol. 308(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:313:y:2024:i:c:s036054422403545x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.