Short term solar irradiance forecasting using a mixed wavelet neural network


The proposed model adopts novel mixed wavelet architecture.

The proposed model incorporates sky clearness index as exogenous input to the forecasting model.

The paper provides the comparison of proposed model with ARIMA, ETS, Persistence and ANN.

Employing wavelets in the ANN architecture allows for high degree of improvement in terms of forecasting error.

The paper provides complete theoretical background of the proposed model along with neural networks and wavelet theory.


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.


  • Solar irradiance;
  • Variability;
  • Neural networks;
  • Wavelets;
  • Tropics

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