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Short term solar irradiance forecasting using a mixed wavelet neural network

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  • 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
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    References listed on IDEAS

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    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. 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.
    4. 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.
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