Solar irradiance forecasting by using wavelet based denoising

Predicting of global solar irradiance is very important in applications using solar energy resources. Due to the fact that in many applications, the data collected includes noise from different sources. The noise probably would have a great influence in the process of building regression models for irradiance forecasting. Denoising based on wavelet transformation as a preprocessing step is proposed to apply to the time series meteorological data. Artificial neural network and support vector machine are then utilized to make predictive model on Global Horizontal Irradiance (GHI) for the three cities located in California, Kentucky and New York, individually. Detailed experimental analysis is presented for the developed predictive models and comparisons with existing methodologies showed that the proposed approach gives a significant improvement with increased generality.

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