Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis

In this paper, artificial neural network is combined with wavelet analysis for the forecast of solar irradiance. This method is characteristic of the preprocessing of sample data using wavelet transformation for the forecast, i.e., the data sequence of solar irradiance as the sample is first mapped into several time-frequency domains, and then a recurrent BP network is established for each domain. The forecasted solar irradiance is exactly the algebraic sum of all the forecasted components obtained by the respective networks, which correspond respectively the time-frequency domains. Discount coefficients are applied to take account of different effect of different time-step on the accuracy of the ultimate forecast when updating the weights and biases of the networks in network training. On the basis of combination of recurrent BP networks and wavelet analysis, a model is developed for more accurate forecasts of solar irradiance. An example of the forecast of day-by-day solar irradiance is presented in the paper, the historical day-by-day records of solar irradiance in Shanghai constituting the data sample. The results of the example show that the accuracy of the method is more satisfactory than that of the methods reported before.