IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v259y2020ics0306261919318094.html
   My bibliography  Save this article

Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?

Author

Listed:
  • Yagli, Gokhan Mert
  • Yang, Dazhi
  • Gandhi, Oktoviano
  • Srinivasan, Dipti

Abstract

Ground-based radiometry is the most reliable method for obtaining data for solar energy applications. However, due to potential calibration and data quality-control issues, as well as the high cost associated with instrumentation, reliable and long-term ground-based measurements are scarce. For this reason, irradiance data from alternative sources, such as modeled or remote-sensed data, are of interest. Nonetheless, data coming from these sources are known to have systematic errors that may deteriorate the performance of subsequent applications. This study aims to evaluate the quality of solar forecasts generated using two types of irradiance data, namely, ground-based and bias-corrected satellite-derived global horizontal irradiance using error decomposition frameworks. Hourly forecasts are generated using 5 high-performance machine-learning models, with ground-based data from 15 research-grade monitoring stations in Europe, South America, and Africa, and satellite-derived data at the collocated pixels, separately. On top of these component forecasts, ensemble forecasts are also evaluated. Instead of limiting the forecast verification to a set of accuracy measures (e.g., mean bias error or root mean square error), the Murphy–Winkler forecast verification framework is considered, in which the joint, conditional, and marginal distributions of observations and forecasts are examined. The framework describes the qualities of forecast, such as calibration, conditional bias, or discrimination, with quantitative measures. Hence, it provides additional insights to the traditional measure-oriented approach. The main results show that the forecasts generated using bias-corrected satellite-derived data have comparable quality as compared to those generated using ground-based data.

Suggested Citation

  • Yagli, Gokhan Mert & Yang, Dazhi & Gandhi, Oktoviano & Srinivasan, Dipti, 2020. "Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318094
    DOI: 10.1016/j.apenergy.2019.114122
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919318094
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.114122?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. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    4. Ren, Ye & Suganthan, P.N. & Srikanth, N., 2015. "Ensemble methods for wind and solar power forecasting—A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 82-91.
    5. Buffat, René & Grassi, Stefano & Raubal, Martin, 2018. "A scalable method for estimating rooftop solar irradiation potential over large regions," Applied Energy, Elsevier, vol. 216(C), pages 389-401.
    6. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2015. "Assessment of direct normal irradiance and cloud connections using satellite data over Australia," Applied Energy, Elsevier, vol. 143(C), pages 301-311.
    7. Yang, Dazhi, 2018. "A correct validation of the National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 152-155.
    8. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    9. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
    10. Antonanzas-Torres, F. & Urraca, R. & Polo, J. & Perpiñán-Lamigueiro, O. & Escobar, R., 2019. "Clear sky solar irradiance models: A review of seventy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 374-387.
    11. Martín-Pomares, Luis & Martínez, Diego & Polo, Jesús & Perez-Astudillo, Daniel & Bachour, Dunia & Sanfilippo, Antonio, 2017. "Analysis of the long-term solar potential for electricity generation in Qatar," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1231-1246.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    2. Sascha Lindig & Atse Louwen & David Moser & Marko Topic, 2020. "Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches," Energies, MDPI, vol. 13(19), pages 1-18, September.
    3. Chen, Shanlin & Li, Chengxi & Xie, Yuying & Li, Mengying, 2023. "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," Applied Energy, Elsevier, vol. 352(C).
    4. Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    5. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Chu, Yinghao & Wang, Yiling & Yang, Dazhi & Chen, Shanlin & Li, Mengying, 2024. "A review of distributed solar forecasting with remote sensing and deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    7. Rodríguez-Benítez, Francisco J. & López-Cuesta, Miguel & Arbizu-Barrena, Clara & Fernández-León, María M. & Pamos-Ureña, Miguel Á. & Tovar-Pescador, Joaquín & Santos-Alamillos, Francisco J. & Pozo-Váz, 2021. "Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery," Applied Energy, Elsevier, vol. 292(C).
    8. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2022. "Ensemble solar forecasting and post-processing using dropout neural network and information from neighboring satellite pixels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    9. Chu, Yinghao & Yang, Dazhi & Yu, Hanxin & Zhao, Xin & Li, Mengying, 2024. "Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?," Applied Energy, Elsevier, vol. 356(C).
    10. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    11. Chen, Shanlin & Liang, Zhaojian & Dong, Peixin & Guo, Su & Li, Mengying, 2023. "A transferable turbidity estimation method for estimating clear-sky solar irradiance," Renewable Energy, Elsevier, vol. 206(C), pages 635-644.

    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. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    2. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    3. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    4. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    5. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2022. "Ensemble solar forecasting and post-processing using dropout neural network and information from neighboring satellite pixels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    6. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    8. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    10. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).
    11. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
    13. Moldovan, Camelia Liliana & Păltănea, Radu & Visa, Ion, 2020. "Improvement of clear sky models for direct solar irradiance considering turbidity factor variable during the day," Renewable Energy, Elsevier, vol. 161(C), pages 559-569.
    14. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
    15. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    16. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    17. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    18. Miriam Steurer & Robert Hill, 2019. "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers 2019-02, University of Graz, Department of Economics.
    19. Yang, Dazhi & Gueymard, Christian A., 2019. "Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    20. Abhnil Amtesh Prasad & Merlinde Kay, 2021. "Prediction of Solar Power Using Near-Real Time Satellite Data," Energies, MDPI, vol. 14(18), pages 1-20, September.

    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:appene:v:259:y:2020:i:c:s0306261919318094. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.