The network of stations for diffuse solar radiation measurements is scarce through the world, while global solar radiations are available for many locations. Since 1960s numerous studies have been developed to model diffuse fraction on the clearness index (that is based on global radiation). Recent comparative studies, based on polynomial regression, corroborated that hourly values of diffuse solar radiation are not very well modeled by only clearness index, even though there is a strong relation. On the other hand, neural network techniques were used to model satisfactorily the hourly values of diffuse radiation combining the clearness index with some environmental parameters such as latitude, longitude, time of the day, month, rainfall, air temperature, relative humidity and atmospheric pressure. Even tough neural network techniques gives satisfactory results, they are not a user-friendly tool for non-experts. In this work we propose a multiple linear regression that takes into account the clearness index, the particulate matter (PM10), the cloud effect and some environmental parameters available in conventional meteorological stations. The model we propose is easier to understand than the neural network and performs better.

Hourly diffuse solar radiation in the presence of clouds and other environmental parameters: the city of São Paulo

FURLAN, CLAUDIA;
2008

Abstract

The network of stations for diffuse solar radiation measurements is scarce through the world, while global solar radiations are available for many locations. Since 1960s numerous studies have been developed to model diffuse fraction on the clearness index (that is based on global radiation). Recent comparative studies, based on polynomial regression, corroborated that hourly values of diffuse solar radiation are not very well modeled by only clearness index, even though there is a strong relation. On the other hand, neural network techniques were used to model satisfactorily the hourly values of diffuse radiation combining the clearness index with some environmental parameters such as latitude, longitude, time of the day, month, rainfall, air temperature, relative humidity and atmospheric pressure. Even tough neural network techniques gives satisfactory results, they are not a user-friendly tool for non-experts. In this work we propose a multiple linear regression that takes into account the clearness index, the particulate matter (PM10), the cloud effect and some environmental parameters available in conventional meteorological stations. The model we propose is easier to understand than the neural network and performs better.
Working Paper n. 2008.17, Department of Statistical Sciences, University of Padua
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2530404
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