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• W2079344209 abstract "Abstract Illuminance measurement do not make up a part of routine measurements in meteorological stations in Brazil, therefore, they are very rare. This information is important for evaluating the potential contribution of natural illumination in commercial buildings, which would significantly reduce the consumption of electric energy that is used for artificial illumination and refrigeration systems. To face this lack of information, different models known as luminous efficacy were created, which made possible the estimation of illuminance in regions where there only exists information on solar irradiation. In general, they are statistical models that empirically correlate the relationship between illuminance and solar irradiation with other meteorological variables and/or sky conditions. In this work, an estimation of hourly luminous efficacy was made by means of several statistical models and by the MLP (multilayer perceptron) artificial neural networks (ANN). The hourly global luminous efficacy was estimated by considering a group of physical variables from the same locality and that were collected in a simultaneous way. The data input of the ANN was the following: dew temperature, precipitable water, sky brilliance index, clearness index of Perez and clearness index. The results were compared with the statistical models of Perez et al. [Perez R, Seals R, Michalsky J. All-weather model for sky luminance distribuition-preliminary configuration and validation. Solar Energy 1993;50(3):235–45], and Robledo [Robledo L, Soler A. Luminous efficacy of direct solar radiation for all sky types. Energy Conversion & Management 2000;41:1769–79], adjusted with local coefficients. The artificial neural network model shows a statistical performance slightly better than these models with RMSE of 5.8% for the city of Recife and 3.6% for Pesqueira." @default.
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• W2079344209 date "2012-12-01" @default.
• W2079344209 modified "2023-09-26" @default.
• W2079344209 title "Measuring and modelling illuminance in the semi-arid Northeast of Brazil" @default.
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• W2079344209 doi "https://doi.org/10.1016/j.renene.2012.05.023" @default.
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