Energies (May 2024)

An Experimental Direct Model for the Sky Temperature Evaluation in the Mediterranean Area: A Preliminary Investigation

  • Edoardo De Cristo,
  • Luca Evangelisti,
  • Claudia Guattari,
  • Roberto De Lieto Vollaro

DOI
https://doi.org/10.3390/en17092228
Journal volume & issue
Vol. 17, no. 9
p. 2228

Abstract

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Since the beginning of the 20th century, many studies have focused on the possibility of considering the sky as a body characterized by an apparent temperature, and several correlations to quantify the apparent sky temperature have been proposed. However, the different models were obtained for specific meteorological conditions and through measurements at specific sites. The available models do not cover all locations in the world, although the evaluation of the sky temperature is fundamental for estimating the net radiative heat transfer between surfaces and the sky. Here, experimental data logged from a regional micrometeorological network (in Italy, within the Lazio region) were processed and used to identify an empirical model for the estimation of the sky temperature in the Mediterranean area. Data relating to atmospheric infrared radiation were used to compute the sky temperature, aiming at identifying a direct correlation with the ambient temperature. Climatic data acquired during 2022 were processed. The proposed correlations were compared with other models available in the literature, including the standard ISO 13790. This study proposes an annual-based direct correlation in its initial phase, demonstrating a superior fit to the measured data compared to well-known direct empirical models from the literature. Subsequently, quarterly-based correlations are introduced further in a secondary phase of the work to improve the model’s adaptation to experimental observations. The results reveal that quarterly-based correlations improve goodness-of-fit indexes compared to annual-based and well-known direct empirical correlations. Finally, a detached building was modeled via a dynamic code to highlight the influence of different correlations on annual energy needs.

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