Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics

• Satellite image analysis is performed and cloud cover index is classified using self-organizing maps (SOM).
• The ESSS model is used to forecast cloud cover index.
• Solar irradiance is estimated using multi-layer perceptron (MLP).
• The proposed model shows better accuracy than other investigated models.

We forecast hourly solar irradiance time series using satellite image analysis and a hybrid exponential smoothing state space (ESSS) model together with artificial neural networks (ANN). Since cloud cover is the major factor affecting solar irradiance, cloud detection and classification are crucial to forecast solar irradiance. Geostationary satellite images provide cloud information, allowing a cloud cover index to be derived and analysed using self-organizing maps (SOM). Owing to the stochastic nature of cloud generation in tropical regions, the ESSS model is used to forecast cloud cover index. Among different models applied in ANN, we favour the multi-layer perceptron (MLP) to derive solar irradiance based on the cloud cover index. This hybrid model has been used to forecast hourly solar irradiance in Singapore and the technique is found to outperform traditional forecasting models.