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

Abstract:

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. © 2013 Elsevier Ltd. All rights reserved.

Año de publicación:

2014

Keywords:

• multi-layer perceptron
• Exponential smoothing state space model
• Satellite image analysis
• Hourly solar irradiance forecasting
• Self-Organizing Maps

Fuente:

scopus