IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v90y2016icp481-492.html
   My bibliography  Save this article

Short term solar irradiance forecasting using a mixed wavelet neural network

Author

Listed:
  • Sharma, Vishal
  • Yang, Dazhi
  • Walsh, Wilfred
  • Reindl, Thomas

Abstract

In modern smart grids and deregulated electricity markets, accurate forecasting of solar irradiance is critical for determining the total energy generated by PV systems. We propose a mixed wavelet neural network (WNN) in this paper for short-term solar irradiance forecasting, with initial application in tropical Singapore. The key advantage of using wavelet transform (WT) based methods is the high signal compression ability of wavelets, making them suitable for modeling of nonstationary environmental parameters with high information content, such as short timescale solar irradiance. In this WNN, a combination of the commonly known Morlet and Mexican hat wavelets is used as the activation function for hidden-layer neurons of a feed forward artificial neural network (ANN). To demonstrate the effectiveness of the proposed approach, hourly predictions of solar irradiance, which is an aggregate sum of irradiance value observed using 25 sensors across Singapore, are considered. The forecasted results show that WNN delivers better prediction skill when compared with other forecasting techniques.

Suggested Citation

  • Sharma, Vishal & Yang, Dazhi & Walsh, Wilfred & Reindl, Thomas, 2016. "Short term solar irradiance forecasting using a mixed wavelet neural network," Renewable Energy, Elsevier, vol. 90(C), pages 481-492.
    • Handle: RePEc:eee:renene:v:90:y:2016:i:c:p:481-492
    DOI: 10.1016/j.renene.2016.01.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116300209
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.01.020?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. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    2. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    3. Kaplanis, S.N., 2006. "New methodologies to estimate the hourly global solar radiation; Comparisons with existing models," Renewable Energy, Elsevier, vol. 31(6), pages 781-790.
    4. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    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. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    2. 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.
    3. Trapero, Juan R., 2016. "Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates," Energy, Elsevier, vol. 114(C), pages 266-274.
    4. Trapero, Juan R. & Kourentzes, Nikolaos & Martin, A., 2015. "Short-term solar irradiation forecasting based on Dynamic Harmonic Regression," Energy, Elsevier, vol. 84(C), pages 289-295.
    5. Zagouras, Athanassios & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods," Renewable Energy, Elsevier, vol. 78(C), pages 203-218.
    6. 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.
    7. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    8. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    9. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
    10. Zagouras, Athanassios & Kolovos, Alexander & Coimbra, Carlos F.M., 2015. "Objective framework for optimal distribution of solar irradiance monitoring networks," Renewable Energy, Elsevier, vol. 80(C), pages 153-165.
    11. Boland, John, 2015. "Spatial-temporal forecasting of solar radiation," Renewable Energy, Elsevier, vol. 75(C), pages 607-616.
    12. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    13. 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).
    14. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    15. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    16. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    17. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    18. Zarzo, Manuel & Martí, Pau, 2011. "Modeling the variability of solar radiation data among weather stations by means of principal components analysis," Applied Energy, Elsevier, vol. 88(8), pages 2775-2784, August.
    19. Voyant, Cyril & Notton, Gilles & Duchaud, Jean-Laurent & Gutiérrez, Luis Antonio García & Bright, Jamie M. & Yang, Dazhi, 2022. "Benchmarks for solar radiation time series forecasting," Renewable Energy, Elsevier, vol. 191(C), pages 747-762.
    20. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.

    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:renene:v:90:y:2016:i:c:p:481-492. 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/renewable-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.